English · Español

---

datosgobdo-mcp

Ask an AI assistant a question about Dominican public data, and get an answer traced back to the government file it came from.

This is a Model Context Protocol server for datos.gob.do, the Dominican Republic's official open-data portal. It plugs into Claude Desktop, Claude Code, Cursor, ChatGPT Desktop or any MCP-compatible client, and lets the model search the catalog, download the actual files, parse them, and run real analysis — without you writing code, opening a URL, or downloading a spreadsheet.

Official source. The canonical repository is alcastaro/datos.gob.do-MCP-server. The only official distributions are the PyPI package dominican-open-data-mcp and the MCP Registry entry io.github.alcastaro/datos.gob.do-MCP-server. Copies published elsewhere are not maintained by the author and may be outdated or modified — verify against this repository before installing.

This README is written to be read in order. Part 1 needs no technical knowledge. Part 2 teaches what MCP actually is, using this server as the worked example. Parts 3 to 6 are the reference and the engineering detail. If you want the same material as a walkthrough, read the Tutorial (Español).

---

Contents

Part 1 — Start here (no technical knowledge needed)

1. What this is, in plain words
2. Quick start
3. The six guided prompts — start with /empezar_aqui
4. What you can ask
5. Read this before you quote a number

Part 2 — Understanding MCP (educational)

6. What is MCP? Tools, resources and prompts
7. What is datos.gob.do?

Part 3 — What this server exposes

8. Tools (24, plus 3 optional)
9. Resources (3) and one resource template
10. Prompts (6)
11. Which primitive to reach for

Part 4 — Why this server exists

12. How it compares with other CKAN MCP servers

Part 5 — Technical reference

13. Installation and client configuration
14. What the answers tell you about themselves
15. Security and environment variables
16. Architecture
17. Measured limitations

Part 6 — Development

18. Development, testing and the MCP Inspector
19. Contributing, credits, how to cite, licence

---

---

Part 1 — Start here

1. What this is, in plain words

The Dominican government publishes thousands of files: public payrolls, budget execution, hospital activity, migration flows, procurement contracts, electricity losses, forest fires. It is all public. Almost nobody reads it, because reading it means knowing which of 266 institutions published what, finding the file, downloading a spreadsheet with the header on row 3, and knowing what to do next.

This server hands that whole job to your AI assistant. You ask in your own words. The assistant finds the dataset, downloads the file from the institution's own server, figures out the columns, runs the sum or the average, and tells you the answer along with where it came from and what it had to leave out.

Three things worth knowing up front, because they shape everything else:

• It only reads. Nothing here can modify the portal or publish anything. There is no login and no password.
• It runs on your computer, alongside your assistant. Your questions do not pass through a server owned by this project.
• It tells you when the data is bad. Roughly half the catalog cannot be downloaded programmatically at all, and the tools say so instead of inventing a number. That is the point of the whole design, not a caveat buried at the bottom.

2. Quick start

You need uv, a small tool that runs Python programs without you installing anything else. On macOS or Linux, paste this into a terminal:

curl -LsSf https://astral.sh/uv/install.sh | sh

On Windows, follow the uv installation page.

Then tell your assistant about the server.

Claude Desktop. Open Settings → Developer → Edit Config, or edit the file directly:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

Paste this, replacing YOUR_USERNAME:

{
  "mcpServers": {
    "datosgobdo": {
      "command": "/Users/YOUR_USERNAME/.local/bin/uvx",
      "args": ["dominican-open-data-mcp"]
    }
  }
}

Use the full path to uvx — Claude Desktop does not read your shell's PATH. Then quit Claude Desktop completely (Cmd+Q on macOS, not just closing the window) and reopen it. Under Settings → Developer you should see datosgobdo running.

Nothing else is required. If you later want to change a setting — the network guard, the cache directory — it goes in an "env" block inside this file, not in your shell: see §13.

Claude Code. One line:

claude mcp add datosgobdo -- uvx dominican-open-data-mcp

Anything else. Same idea: register uvx as the command with dominican-open-data-mcp as its argument. The MCP clients directory lists which clients support which features. Full options — dev versions, local clones, hosted mode — are in §13.

3. The six guided prompts — start with /empezar_aqui

Twenty-four tools is not an invitation. Someone who has never seen this catalog has no way to know that payrolls, budget execution and public investment are the three things it covers best.

So the server ships six prompts: ready-made questions, written to encode the habits that took a full catalog audit to learn. In Claude Code and Claude Desktop they appear as slash commands. Type:

/empezar_aqui

and the assistant will introduce you to the portal, tell you what it covers well, propose three concrete questions you could ask next, and warn you up front about what cannot be downloaded.

The other five take one argument each:

Prompt	You give it	What it does
/empezar_aqui	—	Portrait of the portal and three questions to start with.
/serie_temporal	a topic	Builds a year-by-year series, declaring the real period covered and refusing to treat the year column as a measure.
/auditar_nomina	an institution	Sum, average and salary distribution of a public payroll, declaring how many rows were excluded and why.
/verificar_fuente	a resource URL	Checks scope, provenance and shape of a file before you rely on it.
/explorar_institucion	an institution	Inventory of everything that institution publishes, with the real download status of each file.
/cruzar_fuentes	a topic	Crosses two resources, declaring units, periods and the limits of the join.

If your client does not show prompts as slash commands, see its entry in the MCP clients directory — prompt support is optional for clients, and the MCP Inspector can always show and run them.

4. What you can ask

Plain questions, in Spanish or English. Some that work today:

How many datasets are on the datos.gob.do portal, and which institutions publish the most?

Find the five most relevant budget datasets and tell me which institution publishes each one.

How much does the Judicial Branch spend on salaries?

How many active employees does the Ministry of Agriculture have in April 2026, broken down by employment status?

That last one is worth pausing on, because it is the kind of question the whole analytics layer exists for. The Agriculture payroll is a CSV with 826,000 rows and 94 MB — far too big to paste into a conversation. The server downloads it once, converts it to a columnar cache, and answers with a grouped aggregation: 6 status types, roughly 8,915 employees. The first call takes about 14 seconds; every later question about the same file answers in under half a second.

Compare FONDOMARENA's approved versus executed budget over the last three years.

What columns does the Ministry of Interior's vehicle-theft dataset have?

List the ten most recently updated datasets.

Who this tends to be useful for: data journalists who would otherwise write a scraper; researchers who need programmatic access; transparency groups tracking budget execution and procurement; developers prototyping on public data; public officials checking what their own institution already publishes; and anyone curious about how the state actually operates.

5. Read this before you quote a number

This catalog has real defects, and they were measured — a census of the whole thing on 2026-08-08, one resource per dataset, 1,056 resources over real MCP sessions. Four findings change how you should read any figure you get from here:

About half the catalog cannot be downloaded by a program. 561 of 1,056 resources can be read (53.1 %), up from 540 in the census: 0.14.0's format work recovered 21 of them, re-measured against the live portal on 2026-08-13. The largest single cause of the rest is not this server and no version of it can fix it: 360 resources across 98 institutions sit behind a site configuration that refuses programmatic downloads of the files those same institutions publish as open data. From the same address, 21 other government hosts behind the same CDN answer normally — so it is per-site configuration, not our network. A further 15 links are dead and 6 files are unreadable at any encoding.

One in three multi-format datasets contradicts itself. Of 528 datasets whose formats could be compared, 176 disagree on row count or column count. One example: the Treasury's recaudaciones-sirite-2021-2025 has 971,818 rows as CSV and 197,338 as ODS. A citizen downloading the ODS and a journalist downloading the CSV would quote different numbers from the same official dataset. Practical rule: check more than one format before you publish a total.

Numbers are often stored as text. 93 of the 540 resources readable in that census hold numeric columns as text, usually because a handful of cells say N/A or #REF!. The tools read such a column as numbers where each value permits it and report what that cost — see §14. Read values_excluded before quoting the total.

No dataset declares how often it is updated. The periodicidad field is empty in all 1,056. A dataset labelled "2018-2026" may have been fed last month or frozen two years ago; you have to infer freshness from the last period that actually has data.

None of this is a reason not to use the catalog. It is a reason to cite it precisely — which is what /verificar_fuente and the self-describing response fields are for.

---

---

Part 2 — Understanding MCP

6. What is MCP? Tools, resources and prompts

Model Context Protocol is an open standard — created by Anthropic, now adopted across the industry — for connecting language models to outside data and capabilities. Instead of every application inventing its own plugin format, a model-facing app (the client, e.g. Claude Desktop) talks to any number of servers over one protocol.

A server can offer three kinds of thing. The distinction matters, because it determines who decides when something is used:

Primitive	Controlled by	What it is	In this server
Tools	the model	Functions the model may call, with typed arguments. The model picks when and with what.	24 functions: search, download, aggregate, query…
Resources	the application	Data the app can attach as context, addressed by URI. No side effects, no cost to reason about.	3 documents + 1 URI template
Prompts	the user	Templates the user invokes deliberately, usually as slash commands.	6 guided workflows

The protocol also defines client-side primitives — sampling, elicitation, roots — which this server does not use.

Concept guides: tools, resources, prompts. If you want to build one, start with Build a server, and read Part 3 of our Tutorial for what this project learned doing it.

What this server declares on connection, verified over a live session on 2026-08-12:

{
  "serverInfo": { "name": "datosgobdo-mcp", "version": "0.14.0" },
  "protocolVersion": "2025-11-25",
  "capabilities": {
    "tools":     { "listChanged": false },
    "resources": { "subscribe": false, "listChanged": false },
    "prompts":   { "listChanged": false }
  }
}

listChanged: false and subscribe: false are honest declarations, not omissions: the tool list is fixed at startup, and no resource here changes often enough to be worth a subscription.

On the two version numbers. The spec links above point to 2026-07-28, the current specification, because that is what you should read. The server negotiates 2025-11-25 because it pins mcp>=1.9.0,<2 — SDK 2.0 renamed FastMCP to MCPServer and dropped the old import path with no shim. What 2026-07-28 adds and this server therefore does not implement: server/discover, the per-request _meta fields, and per-request log levels. Migration is tracked, not accidental.

7. What is datos.gob.do?

The Dominican government's official open-data portal, operated by OGTIC. It runs CKAN 2.11.3 — the same platform behind data.gov (USA), data.gov.uk and much of Latin America.

What the portal declares (queried live on 2026-08-12):

Datasets	1,061
Registered organizations	266
Thematic groups	11
Tags	874
CKAN extensions loaded	activity, datosgobdo_theme

What the 2026-08-08 audit measured, which is a different thing and the gap is instructive:

Resources (files) in the catalog	3,826
Organizations that actually own a dataset	261 of the 266 registered
Resources tested (one per dataset)	1,056
Machine-readable	561 (53.1 %) — 540 in the 2026-08-08 census, plus 21 recovered by 0.14.0
Rows downloaded and cached	13,371,601 in the census, plus 82,490 recovered — and 846,388 more in sibling files outside it
Resources hosted on datos.gob.do itself	66 — the rest live on 273 other domains

That last row is the structural fact behind most of this project. The portal is a catalogue of links, not a repository. Each institution keeps its own files on its own web server, so availability, format hygiene and access rules are decided in 273 places the portal does not control.

Note also what the extension list does not include: CKAN's DataStore is not installed here. That single fact is why this server looks the way it does — see §12.

This project was inspired by datagouv-mcp (Etalab, France), but datos.gob.do runs CKAN rather than udata, so the implementation is its own.

---

---

Part 3 — What this server exposes

8. Tools (24, plus 3 optional)

Typed functions, grouped in five families. The data-producing tools (analytics, preview, cache) return typed outputSchema / structuredContent so hosts can validate results; navigational metadata tools return JSON. Every portal-facing tool is annotated readOnlyHint: true; network-facing ones openWorldHint: true.

Every tool answers with one object. Listings name what they carry and count it — {organizations, count, limit_reached}, {tags, count, limit_reached}, {groups, count}, {suggestions, count, kind, query}. limit_reached matters because the caps are lower than the catalog: 200 institutions against 266, and any tag listing without a query is a sample of 874.

Discovery

Tool	What it does
search_datasets	Search datasets by keyword, organization, tag, or group. Combinable filters, pagination.
get_dataset	Full metadata for a dataset: title, description, licence, author, and every resource with its direct download URL.
list_recent_datasets	Datasets sorted by most-recent modification. Useful for monitoring portal updates.
get_site_stats	Portal-wide counts (datasets, organizations, groups, tags).

Resource files

Tool	What it does
get_resource	Metadata for a single resource (URL, format, size, date).
search_resources	Search resources by name.
download_resource_preview	Download a file and return N rows. CSV, TSV, XLSX, XLS, ODS, JSON. 5 MB cap. Sample mode: head / tail / random.
check_resources	Ask up to 25 URLs whether their files can actually be downloaded, without downloading them. Returns a class per URL — reachable, browser challenge, site rule, dead link, no answer — because a catalog entry is not evidence the file is still there.

Analytics

DuckDB over a persistent Parquet cache. The first call per resource downloads and caches (up to 100 MB); later calls are sub-second. The cache is worth roughly 44× on measured medians.

Tool	What it does
get_resource_schema	Column names, inferred types, sample values. The cheap reconnaissance step before any aggregation.
summarize_resource	Auto profile: row count, per-column nulls and distinct counts, min/max/mean on numerics, top-N on categoricals.
filter_resource	Typed WHERE / SELECT / ORDER BY / LIMIT. Ops: =, !=, <, <=, >, >=, in, not_in, contains, starts_with, ends_with, is_null, is_not_null.
aggregate_resource	Typed GROUP BY + aggregations + HAVING + ORDER BY. Fns: count, count_distinct, sum, avg, mean, median, min, max, stddev, variance.
quantiles_resource	Percentile distribution (p25/p50/p75/p90/p95/p99) of numeric columns.
find_duplicates_resource	Rows duplicated on given columns (or all). Essential for payroll and census quality checks.
detect_outliers_resource	Rows outside the IQR fence on a numeric column, sorted by distance from the median.
query_resource	Power-user escape hatch: read-only SQL against table data. SELECT/WITH only; DDL/DML/COPY/PRAGMA/ATTACH/LOAD rejected, and sandboxed (see §15).
save_query_to_csv	Write a filter or SQL result to a local CSV. Absolute destination, or the default ~/Downloads/datosgobdo-exports/. Disabled in hosted mode.
get_cache_stats	On-disk Parquet cache statistics, plus the server's own identity and effective security mode. total_bytes is disk usage, not index usage: orphan_entries counts Parquet files the index does not list — written by a call whose bookkeeping lost the cache lock, or by a process that died before recording them — and a non-zero value there means contention rather than a healthy cache.
clear_cache	Wipe the local Parquet cache. The only non-read-only tool in the server (destructiveHint: true). Disabled in hosted mode.

Catalog

Tool	What it does
list_organizations	Publishing institutions with a dataset count each.
get_organization	Detail for one institution (description, dataset count, URL).
list_groups	Thematic categories with counts.
list_tags	Tags, optionally filtered by prefix.

Autocomplete

Tool	What it does
autocomplete	Resolve partial names for datasets, organizations, groups or tags — for when the user only gives part of a name.

GCP pipeline (optional)

Installed with pip install 'dominican-open-data-mcp[gcp]'; three extra tools register automatically when the Google Cloud libraries are present, taking the count to 27. They make this server the ingestion half of a BigQuery pipeline: discover here, load to BigQuery, then query with Google's own BigQuery MCP for the cross-dataset JOINs a local DuckDB cache cannot do.

Tool	What it does
load_resource_to_bigquery	Resource → Parquet cache → GCS upload → BigQuery external table (default, zero-ETL) or load job.
list_bigquery_exports	List tables in a BigQuery dataset.
get_bigquery_table_info	Schema, row count and source URIs of a table.

Set DATOSGOBDO_GCS_BUCKET to avoid passing the bucket on every call. Preview status: these three are outside the stability promise and have not been exercised against a live project.

9. Resources (3) and one resource template

Resources are addressed by URI and read by the application, not called by the model. They exist here for the facts that are small, stable, and wasteful to spend a tool call on. All three are read-only and free of side effects.

URI	Type	What it holds
datosgobdo://catalog/overview	application/json	Portal totals: datasets, institutions, groups, tags.
datosgobdo://catalog/institutions	application/json	Every publishing institution with its dataset count — the answer to "which institution?" before any query.
datosgobdo://guide/verification	text/markdown	The four fields that make a number checkable, and what to do when they are missing.

That last one is a resource rather than a prompt on purpose: it is not a request to act, it is reference text worth having in context while you work.

One resource template — a URI pattern with a parameter, so one definition addresses every dataset in the catalog:

Template	Fill in	Returns
datosgobdo://dataset/{dataset_id}	a dataset id or slug	That dataset's metadata as attachable context.

Example: datosgobdo://dataset/nomina-poder-judicial.

How to use them. In Claude Desktop, resources appear in the attachment menu of a conversation with the server connected. In other clients, check the clients directory — resource support is optional. In every client, the Inspector's Resources panel lists them and shows the raw payload, including expanding the template.

10. Prompts (6)

Prompts are user-controlled: nothing invokes them but you. Each one here encodes a habit learned the hard way during the catalog audit — which is why they are worth using even when you know the tools well.

Prompt	Argument	The habit it encodes
empezar_aqui	—	Orientation before exploration, and the download warning stated up front rather than discovered later.
serie_temporal	tema (required)	Declare the real period the data covers, not the one in the title; never treat the year column as a measure.
auditar_nomina	institucion (required)	Report excluded rows and their provenance alongside any salary total.
verificar_fuente	url (required)	Check scope, provenance and shape before relying on a file.
explorar_institucion	institucion (required)	Inventory with each file's real download status, not just its catalog entry.
cruzar_fuentes	tema (required)	State units, periods and join limits before crossing two sources.

How to invoke them. In Claude Code and Claude Desktop, as slash commands: /empezar_aqui, or /serie_temporal and then the topic when asked. Some clients present them in a menu instead. In the Inspector, the Prompts panel lists each one with its arguments and renders the expanded text before anything is sent to a model — the most reliable way to see exactly what a prompt does.

11. Which primitive to reach for

You want to…	Use	Why
Answer a specific question about data	a tool, via ordinary conversation	The model chooses and combines them.
Start from zero, or follow a rigorous method	a prompt	Six workflows with the caveats already built in.
Give the assistant standing background	a resource	Attach it once; no tool call, no tokens spent deciding.
Pin one dataset as context	the resource template	datosgobdo://dataset/{id}.
Do something the typed tools do not cover	query_resource	Read-only SQL, sandboxed. The escape hatch, not the first move.

---

---

Part 4 — Why this server exists

12. How it compares with other CKAN MCP servers

CKAN powers hundreds of government portals, so a generic CKAN MCP server is an obvious idea and a good one. The most developed is ondata/ckan-mcp-server (MIT, TypeScript, adopted by AgID, Italy's digital agency): dataset search with full Solr syntax, organizations and groups, discovery across ~950 portals, and tabular access through CKAN's DataStore API. It points at any portal via a server_url argument. If your portal has DataStore populated, use it — it is broader than this project and more actively released.

The difference is not quality, it is where the data lives. Verified live on 2026-08-12:

GET /api/3/action/status_show   → extensions: ["activity", "datosgobdo_theme"]
GET /api/3/action/datastore_search
  → 400  "Action name not known: datastore_search"
resources with datastore_active: 0 / 254 sampled

datos.gob.do has no DataStore. There is no datastore_search, no SQL endpoint, and not one resource is loaded into it. A generic CKAN MCP server pointed here can search metadata perfectly well and cannot read a single row of data. That is not a flaw in it — the extension is optional in CKAN and this portal never enabled it.

So the two projects divide along a real line:

	Portals with DataStore	Portals that are file catalogs
Where the data is	Loaded into CKAN, queryable by API	Files on 273 institutional web servers
How to read it	datastore_search_sql	Download, sniff the encoding, parse, cache, query
Best tool	ondata/ckan-mcp-server	this one

Everything that makes this codebase larger than a CKAN API wrapper exists because of that right-hand column: encoding detection scored by the Spanish it recovers, streaming ODS parsing (loading the full DOM multiplied memory by ~580×), a Parquet cache keyed on the parser build, numeric coercion that declares what it excluded, page→file resolution for the 37 URLs that answer with HTML, an SSRF guard for downloads reaching 273 third-party hosts, and an optional archived-copy fallback that always says when it fired.

If you are building for another Latin American portal, check status_show first. If DataStore is absent — as it is in the Dominican Republic — the file-reading pipeline in this repository is the part you will need, and the Tutorial documents it so it can be reused.

---

---

Part 5 — Technical reference

13. Installation and client configuration

Option A — uvx from PyPI (recommended)

Package: dominican-open-data-mcp.

uvx dominican-open-data-mcp

A short alias binary ships too — both launch the same server:

uvx --from dominican-open-data-mcp datosgobdo-mcp

uvx downloads the package, builds an isolated venv and runs it. First run takes seconds; later runs are instant.

Upgrading from ≤ 0.7.0? Those releases pinned mcp>=1.9.0 with no upper bound, and MCP Python SDK 2.0 (2026-07-28) removed the mcp.server.fastmcp import path — a fresh install fails with ModuleNotFoundError. Install 0.7.1 or later, or pin it yourself: uvx --with "mcp<2" --from dominican-open-data-mcp datosgobdo-mcp.

Option B — uvx from GitHub (development version)

uvx --from git+https://github.com/alcastaro/datos.gob.do-MCP-server.git datosgobdo-mcp

Option C — local clone (for development)

git clone https://github.com/alcastaro/datos.gob.do-MCP-server.git
cd datos.gob.do-MCP-server
uv sync
uv run datosgobdo-mcp   # stdio; Ctrl+C to exit

macOS note: do not clone inside ~/Library/CloudStorage/GoogleDrive-* or similar. macOS blocks executing binaries from cloud-synced paths (a TCC restriction). Use ~/code/ or equivalent.

Client configuration

Claude Desktop and Claude Code are covered in §2. To track the development version in Claude Desktop, replace the args with ["--from", "git+https://github.com/alcastaro/datos.gob.do-MCP-server.git", "datosgobdo-mcp"]; in Claude Code, claude mcp add datosgobdo -- uvx --from git+https://github.com/alcastaro/datos.gob.do-MCP-server.git datosgobdo-mcp.

For Cursor and others, the principle is identical — register uvx as the command. Each client's config file location is in its own docs; the MCP clients directory is the index.

Passing settings to the server: the env block

Every DATOSGOBDO_* variable in this README goes in an "env" object inside the client's config:

{
  "mcpServers": {
    "datosgobdo": {
      "command": "/Users/YOUR_USERNAME/.local/bin/uvx",
      "args": ["dominican-open-data-mcp"],
      "env": {
        "DATOSGOBDO_NETGUARD": "strict",
        "DATOSGOBDO_CACHE_DIR": "/Users/YOUR_USERNAME/.cache/datosgobdo-mcp"
      }
    }
  }
}

export DATOSGOBDO_NETGUARD=strict in your shell does not reach the server. A stdio MCP server launched by a client inherits only a limited, platform-dependent subset of the environment — MCP debugging guidance is explicit about it. Set the variable in your shell and the server starts in the default mode while you believe it is locked down. This matters most for DATOSGOBDO_NETGUARD, which is a security control (§15).

Two consequences of the same fact, both worth knowing before you file a bug:

• Use absolute paths for every path-valued setting. The working directory of a client-launched server is undefined — / on macOS. DATOSGOBDO_ARCHIVE_DIR=mi-archivo resolves nowhere, and the server now logs is not a directory … Archive fallback stays off rather than going quiet. Same for DATOSGOBDO_CACHE_DIR and for the dest argument of save_query_to_csv, which refuses a relative path outright.
• uv run datosgobdo-mcp in a terminal behaves differently — there the working directory is wherever you ran it, and your shell environment does apply. A bug that only appears under the client is usually this.

For Claude Code, pass them with -e: claude mcp add datosgobdo -e DATOSGOBDO_NETGUARD=strict -- uvx dominican-open-data-mcp.

Hosted mode (experimental)

DATOSGOBDO_TRANSPORT=streamable-http serves MCP over HTTP (stateless, for horizontal scaling) instead of stdio. In this mode save_query_to_csv and clear_cache are disabled — they touch the server's filesystem and shared cache — and cache statistics omit server paths.

Logs are your problem in this mode. Under stdio the client captures the server's stderr and writes it to a file you can tail; over Streamable HTTP it does not. Collect stderr yourself, or wire up OpenTelemetry, and use ordinary HTTP tooling (curl, a browser's Network panel) to inspect requests and SSE streams.

Variable	Default	Meaning
DATOSGOBDO_TRANSPORT	stdio	streamable-http for hosted deployments.
DATOSGOBDO_HOST / DATOSGOBDO_PORT	127.0.0.1 / 8000	HTTP bind address.
DATOSGOBDO_DUCKDB_MEMORY	2GB	DuckDB memory ceiling per connection.
DATOSGOBDO_DUCKDB_THREADS	4	DuckDB thread cap.
DATOSGOBDO_QUERY_TIMEOUT	0 (off)	Wall-clock seconds before a DuckDB run is interrupted. Covers both query_resource SQL and the conversion of a freshly downloaded file into Parquet.

14. What the answers tell you about themselves

Three fields appear in responses when the server had to do something the caller did not ask for. Each exists because a tool used for auditing must not quietly paper over a defect in the data.

numeric_coercion — a column stored as text was read as numbers.

The most common defect in this catalog: 93 of the 540 resources readable in the 2026-08-08 census hold numeric columns as text, because a handful of cells say N/A or #REF! and that is enough to make a whole payroll column non-numeric. aggregate_resource, quantiles_resource and detect_outliers_resource read such a column as numbers where each value permits it, and report what it cost:

"numeric_coercion": [{
  "column": "SUELDO BRUTO (RD$)", "coerced": true,
  "values_used": 21469, "values_excluded": 37,
  "excluded_values": [{"value": "N/A", "count": 21}, {"value": "#REF!", "count": 16}]
}]

Read values_excluded before quoting the total. A column less than 90 % parseable is left as text and the reply says why, rather than answering a question about a measure from an arbitrary subset of rows. count and count_distinct are never coerced.

linked_files — the URL served a page, and the page linked data files.

37 catalog resources answer with a web page instead of a file. When one linked file clearly matches the request it is fetched, and cache.resolved_from records {page, followed} — you asked for one URL and received data from another, which the reply says rather than hides. When several candidates are indistinguishable they come back as linked_files with names and scores, for you to choose and call again. Files named clss.csv and xls.csv both exist in this catalog; guessing between them would be inventing.

A file the page opens from JavaScript counts as linked. Some portals put the address in onclick="window.location.assign('…')" and nowhere else — the Tribunal Constitucional publishes all three of its formats that way — so reading only anchors reported "no data file on it" about a page anyone can download from in one click.

cache.format_corrected — the catalog's declared format was wrong, and the reply says which way.

The format in the catalog is a claim about the file, and 83 of 1,595 sibling resources have it wrong in both directions: a spreadsheet registered as CSV, and a CSV registered as ODS. The container is identified from what is inside it — the mimetype member for ODS, a workbook part for XLSX — never from the signature alone, because PK is how both start. A ZIP holding exactly one data file is unpacked and detected_from names the member; a ZIP holding several is left alone, because deciding which one is "the data" would be inventing. source_sha256 always covers what the portal served, so a re-download can be compared against it even when what was parsed came from inside an archive.

A pre-2007 .xls (BIFF/OLE2) cannot be read at all and says so, with what to ask the publisher for. It is the worst-served format in the catalog: 12 of 22 readable.

A note on the CSV save_query_to_csv writes. It is UTF-8 with CRLF line endings and no BOM. That is a correct CSV, and Excel on a Spanish-language Windows will still open it as cp1252 and show Año for Año, because without a BOM that is what Excel assumes. The file is fine; the tool most of this audience will open it with is the problem. Two ways around it: open it through Excel's Data → From Text/CSV, which asks for the encoding, or use LibreOffice, which detects UTF-8. Measured on Windows 11: 4E 6F 6D 62 72 65 2C 41 C3 B1 6F 0D — Nombre,Año\r, valid UTF-8, no EF BB BF.

cache.provenance — the answer came from an archived copy rather than the portal.

Government links rot: the 2026-08-08 census found 15 resource URLs already dead and 98 institutions whose sites refuse programmatic access, so a figure you cite today may be uncheckable next year. Point DATOSGOBDO_ARCHIVE_DIR at a directory holding a manifest.json and its Parquet files, and when a portal cannot be reached the server answers from the archived copy. It is off by default, the portal is always tried first, and the reply always says so — cache.provenance carries the capture date, the sha256, the licence and why the origin was not used. A tool that quietly returned yesterday's copy as today's would stop being useful for an audit.

An archive only holds what could be downloaded, so it does not contain the resources a portal refuses. That is the natural assumption and it is wrong.

Variable	Default	Meaning
DATOSGOBDO_ARCHIVE_DIR	unset (off)	Absolute path to a directory with manifest.json + Parquet copies to fall back on.

Set it in the client's env block (§13), with an absolute path. If the directory does not exist the server logs a warning and leaves the fallback off — it will not pretend to be armed.

15. Security and environment variables

Full policy, threat model and reporting process: SECURITY.md. In brief:

• Read-only toward the portal. No authentication, no package_create, no resource_create. The only mutating tool is clear_cache, over the local cache.
• Two injection surfaces, both closed. User values entering CKAN fq filters pass through Solr escaping; every column identifier reaching DuckDB passes an allowlist regex plus a denylist of comment and terminator substrings, then is double-quoted.
• query_resource is sandboxed. Beyond validating that the statement is a single read-only SELECT/WITH, the resource is materialized into an in-memory table and then enable_external_access=false + lock_configuration=true are set before the user's SQL runs — so DuckDB table functions (read_text, read_csv, glob, …) cannot reach the filesystem or the network.
• SSRF guard on every download, initial URL and each redirect hop: http/https only, and every address the hostname resolves to must be globally routable. Cloud metadata (169.254.169.254), loopback, RFC-1918, link-local and IPv6 ULA are blocked. The guarded path covers the metadata HEAD probe as well as the download itself.
• Byte caps on remote fetches (5 MB preview, 100 MB analytics), streamed — bounding memory and decompression-bomb exposure.
• save_query_to_csv requires an absolute .csv/.tsv destination, rejects .. and system paths, and writes with O_NOFOLLOW.

Variable	Values	Meaning
DATOSGOBDO_NETGUARD	public-only (default) / strict / off	strict restricts hosts to datos.gob.do and subdomains; off disables the guard.
DATOSGOBDO_ALLOW_HOSTS	comma-separated, *. wildcards	Operator-trusted hosts — the escape hatch for forks pointing at another CKAN portal.

Set these in the client's env block, not in your shell — §13 shows the exact JSON. A stdio server inherits only a limited subset of the environment, so export DATOSGOBDO_NETGUARD=strict leaves the server running with the default guard. There is no warning for this, because from the server's side nothing happened. To check: get_cache_stats reports the mode actually in force as server.netguard_mode, and the startup line in the client's log records the effective mode.

The default is deliberately not a host allowlist: as §7 shows, legitimate resources live on 273 ministry sites, buckets and CDNs.

On the new primitives: prompts here are static templates with arguments interpolated into text — they perform no I/O. Resources are read-only reads of portal metadata. Neither adds a write path.

16. Architecture

src/datosgobdo_mcp/
  server.py        FastMCP server: 24 tools, 3 resources, 1 template, 6 prompts
  ckan.py          CKAN client: requests, Solr escaping, formatters, provenance
  analytics.py     DuckDB layer: typed query builders, coercion, SQL validation
  download.py      Capped streaming download, fetch headers, encoding detection
  cache.py         Parquet cache + index, keyed on source and parser build
  preview.py       Row-level preview parsers (CSV/TSV/XLSX/XLS/ODS/JSON)
  pagelink.py      Resolves a page URL to the data file it links
  archive.py       Archived-copy fallback with declared provenance
  reachability.py  check_resources: classifies why a URL cannot be read
  netguard.py      SSRF guard for URLs and every redirect hop
  models.py        Pydantic output models (typed outputSchema)
  gcp.py           Optional BigQuery/GCS pipeline tools

Design decisions

• FastMCP over the low-level SDK. Tools are functions decorated with @mcp.tool() and typed via Pydantic: less boilerplate, automatic argument validation.
• DuckDB + Parquet instead of pandas. Columnar cache, SQL engine, streaming from disk. A 94 MB payroll answers grouped aggregations in under a second warm, and memory stays bounded.
• The cache key includes the parser build — package version plus DuckDB's, because DuckDB's sniffer decides column types. A parser upgrade must not serve types inferred by the old one.
• DataStore is absent, so files are parsed client-side. See §12. This is the single decision the rest of the architecture follows from.
• Encoding is scored, not guessed. Candidate decodings are ranked by the Spanish they recover, rather than trusting a confidence number — the fix for live mojibake like A¤o for Año.
• ODS is parsed by streaming content.xml. Loading the full DOM turned a 0.70 MB file into 0.41 GB of RSS; ODS is roughly a third of this catalog, so the naive path was untenable.
• Blocking work runs in asyncio.to_thread (ODS transcode, encoding detection, Parquet COPY) so a long parse never stalls the event loop.
• Defensive truncation. Long descriptions — some institutions publish 5+ KB per organization — are cut to 300 characters in list responses, so one call cannot burn thousands of tokens of context.
• list_recent_datasets is reoriented. CKAN exposes recently_changed_packages_activity_list, but it returns un-hydrated activities ({object_id: "uuid", activity_type: "changed package"}) the model cannot interpret. We use package_search?sort=metadata_modified+desc and return formatted datasets in one call.
• All logging to stderr, and none over the protocol. Per the MCP debugging guidance, a stdio server must never write to stdout — it corrupts the protocol stream. The protocol's own logging channel (notifications/message) was never used here, and as of spec 2026-07-28 it is deprecated: stderr is now what the specification recommends. Nothing to migrate — but do not "improve" this by adding protocol logging.

Stack

mcp (official Python SDK, FastMCP) · duckdb · httpx · openpyxl (read-only streaming XLSX) · pydantic · stdlib csv, json, xml.etree (streaming ODS).

17. Measured limitations

Measured against the whole catalog on 2026-08-08 — 1,056 resources, one per dataset, over real MCP sessions — not estimated. Every resource that had failed for a reason inside this server's control was re-measured against the live portal on 2026-08-13, after 0.14.0's format work; site refusals and 4xx were not retried, because nothing changed on our side that could affect them.

Not everything published is reachable. 561 of 1,056 resources can be read — 540 in the census, plus 21 recovered by 0.14.0, worth 82,490 rows. The recovery is exact rather than estimated, so the breakdown moves with it: of the 37 that served a web page, 19 now resolve to the file the page links, leaving 18; of the 8 unreadable files, 2 now parse, leaving 6.

cause	resources	can this server fix it?
Site configuration refuses programmatic downloads	360 across 98 institutions	No. From one address, 21 other government hosts behind the same CDN serve us normally, so it is per-site configuration rather than our network.
Failed at transport level (cause not attributable)	85	Not established
Serves a web page with no data file on it	18	No — catalogue entries pointing at a landing page
Dead link	15	No
Unreadable file	6	Two are pre-2007 .xls, which needs a new reader
CDN whose origin does not answer	6	No
Portal error	5	No

561 + 495 = 1,056. The 360 refusals are untouched by any of this and no version of this server can change them.

A second recovery does not appear in that count, and is larger. 0.14.0 also reads 11 sibling files — a second or third format of a dataset whose one-per-dataset representative was already counted — worth 846,388 rows, among them 622,630 in SeNaSa's ODS and the payroll and housing files MAP, MIVHED and MESCyT publish as JSON. They are excluded from the 561 on purpose: the census measures one resource per dataset, and counting siblings would compare against a denominator that never included them. What it means in practice is that a dataset whose CSV is unreadable may now be readable in another format — §14 explains how cache.format_corrected says so when it happens.

What is established: those sites refuse programmatic access to their own open data from the address measured. What is not established: that a person with a browser in Santo Domingo is refused. That test needs a Dominican residential vantage point and has not been run.

Formats. CSV, XLSX and ODS all read at roughly 93 % of what downloads. JSON was the weakest by a wide margin until 0.14.0 — read_json_auto rejected as malformed what these portals actually publish, which is often a record array wrapped in a metadata envelope, or one object per line — and the files recovered on 2026-08-13 are mostly of that kind. Legacy .xls (BIFF/OLE2) remains the worst served and cannot be read at all: 12 of 22. PDF is not parsed; only its download URL is exposed.

Size. download_resource_preview caps at 5 MB; analytics tools at 100 MB. A single value larger than 16 MB exceeds DuckDB's limit and the file cannot be parsed.

Shape. 41 resources put a title or logo above the real header row, which garbles the auto-detected schema — inspect with download_resource_preview and project columns explicitly. 25 come back with generic column names (column00, unnamed). 93 hold numbers as text, handled and declared per §14.

Formats can disagree with each other. 176 of 528 comparable multi-format datasets differ in row or column count, and in 11 cases one format is empty while another carries the full table. Reading a single format is not evidence of what the dataset contains.

Encoding is effectively solved: one file in the census's 540 still shows damaged accents, and that file is encoded in two codepages at once, so no single reading is correct for it.

Freshness cannot be read from metadata. periodicidad is empty for all 1,056 datasets.

Windows: tested on 2026-08-13, and here is exactly how far. Windows 11 (build 26200), Python 3.13, Defender's real-time protection on, a non-administrator account. The suite runs green — 518 passed, 5 skipped, the one skip being a POSIX-only O_NOFOLLOW test. Encoding holds end to end: a cp1252 payroll comes back with Año and UREÑA intact, 135 of 200 institution names carry non-ASCII and none arrive mangled, and paths with accents and spaces work. An aggregation over a 108,038-row payroll matched an independent Decimal recomputation to the cent. Defender cost nothing measurable — the 40 MB cold read is dominated by the publisher's ~1 MB/s, and repeated raw downloads varied more between themselves than Windows differed from macOS.

What is still not tested on Windows, and therefore not claimed: a user profile that is itself accented (C:\Users\José Pérez\, common in the Dominican Republic — only accented sub-folders were exercised), a Downloads folder redirected into OneDrive, Claude Desktop as the client (the transport was driven by a different MCP client), Windows installed on a drive other than C:, and a Defender exclusion measured before-and-after, which needs administrator rights. The Windows-only branch of the cache lock is likewise still awaiting a run on Windows: its retry policy is tested, its four-line msvcrt shim is not.

Untested, and therefore not claimed: the hosted streamable-http transport under real load, the three GCP tools against a live project, and concurrent use beyond four processes.

---

---

Part 6 — Development

18. Development, testing and the MCP Inspector

Local setup

git clone https://github.com/alcastaro/datos.gob.do-MCP-server.git
cd datos.gob.do-MCP-server
uv sync
uv run pytest          # hermetic by default: no network required

The MCP Inspector

The MCP Inspector is the protocol's own developer tool. It speaks MCP directly, so it shows what the server actually exposes with no model in between — the best way to see tools, resources, templates and prompts as the protocol sees them. Requires Node 22.19+ and installs nothing permanent:

# The published package — no clone needed
npx -y @modelcontextprotocol/inspector uvx dominican-open-data-mcp

It prints a URL carrying a one-time token. Open it for four panels:

• Tools — all 24 with their schemas. Call one and read the raw structuredContent, including numeric_coercion, source_sha256 and computation.
• Resources — the three URIs and the datosgobdo://dataset/{dataset_id} template, with raw payloads.
• Prompts — the six, with their arguments, rendered to their expanded text before anything reaches a model.
• Monitoring — live JSON-RPC traffic in both directions.

From a clone, scripts/inspector.sh wraps both cases:

./scripts/inspector.sh                                        # published package
./scripts/inspector.sh dist/dominican_open_data_mcp-*.whl     # a local build
./scripts/inspector.sh --cli --method tools/list --format json

The local-build path needs that wrapper: the Inspector reads everything after the server command as its own flags, so uvx --from ./dist/….whl … fails with Connection closed because --from never reaches uvx.

CLI mode exits with meaningful codes — 0 success, 3 needs auth, 4 unreachable, 5 the tool returned an error — so it drops straight into CI:

npx -y @modelcontextprotocol/inspector --cli uvx dominican-open-data-mcp \
  --method tools/list  --format json | jq -r '.result.tools[].name'
npx -y @modelcontextprotocol/inspector --cli uvx dominican-open-data-mcp \
  --method prompts/list --format json | jq -r '.result.prompts[].name'
npx -y @modelcontextprotocol/inspector --cli uvx dominican-open-data-mcp \
  --method resources/templates/list --format json

Logs

Claude Desktop writes one log file per server, plus its own:

tail -f ~/Library/Logs/Claude/mcp-server-datosgobdo.log   # macOS — this server
tail -n 20 -F ~/Library/Logs/Claude/mcp*.log              # macOS — all servers + the client

type "$env:AppData\Claude\logs\mcp*.log"                  # Windows

The server logs startup (endpoint, transport, network-guard mode, archive on or off), cache hits and misses, page→file substitutions, suspicious parse shapes, misconfigured environment variables, fatal errors with traceback, and shutdown — all to stderr, which the client captures. Under DATOSGOBDO_TRANSPORT=streamable-http it does not: see §13.

Logs contain resource URLs, cache keys and destination paths. They contain no credentials — the server holds none for the portal — and the optional GCP tools authenticate through your own ADC, which is never logged.

When the client itself is the suspect rather than the server, Claude Desktop can open Chrome DevTools: write {"allowDevTools": true} to ~/Library/Application Support/Claude/developer_settings.json, then Cmd-Option-I. The Console panel shows client-side errors, the Network panel shows message payloads and timing.

Iteration

1. Commit and push to main.
2. Clear the uvx cache to force a refresh: uv cache clean dominican-open-data-mcp (keyed on the distribution name, not the binary name).
3. Restart the MCP client.

For faster loops, point the client at your clone: command: /path/to/clone/.venv/bin/datosgobdo-mcp.

Manual check against the live API

uv run python -c "
import asyncio
from datosgobdo_mcp import ckan
print(asyncio.run(ckan.get_site_stats()))
asyncio.run(ckan.close_client())
"

19. Contributing, credits, how to cite, licence

Contributing

Pull requests welcome. Areas where help would land well:

• Header detection. 41 resources put a banner above the real header row. In XLSX this can cost the whole file: precios_productos_primera_necesidad (PROCONSUMIDOR) carries 890 rows in a sheet declaring dimension A1:K890, and reads as 1 column and 0 rows because cell A1 is a title. The CSV sibling recovers all 890 rows but names them column00…column10. Detecting and skipping the banner would recover real data.
• Cross-format reconciliation. Given a dataset with several formats, pick the trustworthy one rather than the first one listed.
• JSON parsing, the weakest format here.
• Generalizing ckan_endpoint so the same file-reading pipeline serves other DataStore-less portals in the region.
• Windows testing, currently unclaimed.

Credits

Developed by Alberto Castillo Aroca (@alcastaro) with contributions from Juana Casique (@juanacasique).

Data published by the institutions of the Dominican State via datos.gob.do, a portal operated by OGTIC.

Inspired by datagouv-mcp (Etalab, Government of France). For CKAN portals that do have DataStore enabled, ondata/ckan-mcp-server is the reference implementation and worth using instead — see §12.

How to cite

If you use this server — or a figure obtained through it — in an article, report, dataset or talk, please cite it. GitHub's "Cite this repository" button reads CITATION.cff and offers APA and BibTeX directly.

Castillo Aroca, A. (2026). dominican-open-data-mcp: an MCP server for datos.gob.do [Computer software]. OLDS — Observatorio Latinoamericano de Desarrollo Sostenible. https://github.com/alcastaro/datos.gob.do-MCP-server

This is a request, not a licence condition: the MIT terms are unmodified, so nothing here restricts your use. Citation matters for a different reason — figures from this catalog carry caveats (what a numeric coercion excluded, which files could not be downloaded at all), and a citation is how a reader gets back to them.

Cite the institution too. This server reads data; it does not produce it. Every figure belongs to the Dominican government body that published it, and get_dataset returns that institution's name for exactly this purpose.

Licence

MIT. See LICENSE.

Data accessed through this MCP is subject to the licence under which each Dominican institution publishes it on datos.gob.do. Verified across the catalog: 1,020 datasets are ODbL, 15 CC-BY, 6 PDDL, 3 other public-domain terms, and 12 declare no licence at all — those twelve should stay out of any redistribution.