Drop MCP

Drop a skills/ and prompts/ folder, get a FastMCP server — with a browseable catalog — in one line.

import dropmcp

dropmcp.run(skills="skills", prompts="prompts")

dropmcp is the reusable, repo-agnostic engine behind several internal skills/prompts MCP servers, extracted as a standalone library.

Features

• Browseable skill catalog — a built-in web UI to search, filter, and preview every skill and prompt, with copy-paste install snippets for each MCP client.
• Zero-boilerplate server — point it at a skills/ and prompts/ folder and get a tested FastMCP server over streamable-HTTP.
• Filesystem as source of truth — skills and prompts are just markdown with YAML frontmatter; no registry to drift out of sync.
• Observability built in — per-invocation structured logs plus optional OpenTelemetry metrics and traces.
• Agent feedback loop — a record_feedback tool and triage UI for when agents get corrected.
• Per-user subscriptions — optional opt-in so each user's agent sees only a curated subset of the catalog.

Why dropmcp?

Skills and prompts are just markdown — anyone can write them, and there are plenty of ways to get them in front of an agent: copy them into .cursor/skills, ship an IDE plugin, sync a folder, paste them into context. The trouble is that every one of those is tied to a single tool, has no central updates, no way to scope who sees what, and no signal about what actually gets used. Spread across many people, repos, and editors, that fragments fast — the same skill forked five ways with no canonical copy, and skills that work in one agent but not the next.

Serving skills over MCP solves the delivery problem generically: one server is reachable from any MCP client (Cursor, Claude Code, CI, whatever comes next), the filesystem stays the single source of truth so there's no registry to drift, and because every skill call is one request, usage is observable for free. The catch is that standing up that server is the same boilerplate every time — frontmatter parsing, tool/prompt/resource registration, a browseable catalog, telemetry, hosting.

dropmcp is that boilerplate, extracted and built around Fast MCP. Point it at a skills/ and prompts/ folder and you get a tested, observable MCP server, so you run one focused server per audience instead of rebuilding the engine each time. Authors maintain content, not infrastructure.

Install

pip install dropmcp

Optional OpenTelemetry export:

pip install "dropmcp[otel]"

Quick start

Lay out your content:

skills/
  my-skill/
    SKILL.md         # YAML frontmatter: name, category, description
    reference.md     # optional supporting files -> resource links
prompts/
  my-prompt/
    PROMPT.md        # YAML frontmatter: name, description, arguments
    assets/          # optional assets -> prompt://my-prompt/assets/<file>

Then serve it over streamable-HTTP:

import dropmcp

dropmcp.run(skills="skills", prompts="prompts")              # binds 127.0.0.1:8000
dropmcp.run(skills="skills", prompts="prompts", host="0.0.0.0", port=8000)

dropmcp is a hosted server — it exists to share skills and prompts with remote MCP clients, so it serves over streamable-HTTP only (no local stdio). The catalog UI is at http://<host>:<port>/ and the MCP endpoint at /mcp.

Need to customise the server before it runs? Use the factory:

mcp = dropmcp.create_server(skills="skills", prompts="prompts")
# add your own routes / middleware ...
mcp.run(transport="streamable-http", host="0.0.0.0", port=8000)

A runnable example lives in examples/.

Scaffold a new server (copier)

Generate a ready-to-run project from the bundled template:

pip install copier
copier copy gh:agoda-com/dropmcp//template my-skills-mcp
cd my-skills-mcp
pip install -r requirements.txt
python server.py

The template asks for a project name and whether to include Promptfoo eval scaffolding under tests/.

Configuration

Every option can be passed as a keyword argument, set via a DROPMCP_* environment variable, or left to its default (kwargs win, then env, then default).

If an INSTRUCTIONS.md sits next to your content folders it is picked up automatically; otherwise a generic default ships with the package. The {{INSTRUCTION_SUMMARIES}} and {{PROMPT_SUMMARIES}} placeholders are filled from each item's instruction_summary frontmatter.

Hosting guide

dropmcp serves over streamable-HTTP for multi-client hosted deployments. Run your server.py:

DROPMCP_HOST=0.0.0.0 DROPMCP_PORT=8000 python server.py

The catalog UI is available at http://localhost:8000/, the health check endpoint at http://localhost:8000/health, and the MCP endpoint at http://localhost:8000/mcp. Point your remote MCP clients there.

Docker

A minimal Dockerfile for a hosted deployment:

FROM python:3.12-slim

WORKDIR /app

RUN pip install dropmcp

COPY skills/ skills/
COPY prompts/ prompts/
COPY INSTRUCTIONS.md .          # optional

ENV DROPMCP_HOST=0.0.0.0
ENV DROPMCP_PORT=8000
ENV DROPMCP_NAME="My Skills MCP"

EXPOSE 8000
CMD ["python", "-m", "dropmcp"]

Build and run:

docker build -t my-skills-mcp .
docker run -p 8000:8000 my-skills-mcp

Connect a remote MCP client to http://<host>:8000/mcp.

Environment-only deployment

All settings can be passed via environment variables and started with python -m dropmcp — no server.py needed:

export DROPMCP_SKILLS=/data/skills
export DROPMCP_PROMPTS=/data/prompts
export DROPMCP_HOST=0.0.0.0
export DROPMCP_PORT=8000
export DROPMCP_NAME="Acme Skills"
export DROPMCP_WEBSITE_URL="https://skills.example.com"

python -m dropmcp

OpenTelemetry

Install the OTEL extra and point at your collector:

pip install "dropmcp[otel]"
export OTEL_EXPORTER_OTLP_ENDPOINT="http://otel-collector:4318"
python -m dropmcp

Metrics and structured logs are emitted per skill invocation, prompt render, resource read, and MCP protocol event (initialize, tools/list). Metric names use dotted OTel style (for example skill.invocations, skill.invocation.duration) with delta temporality for histograms. The OTEL service name defaults to DROPMCP_NAME; override with OTEL_SERVICE_NAME if needed.

When OTEL_EXPORTER_OTLP_ENDPOINT is unset, OpenTelemetry export is a no-op — no extra imports, no export overhead — but structured per-invocation logs still go to the console.

Agent feedback

dropmcp includes a built-in feedback loop for when agents get corrected and for reusable work agents discover after invoking skills:

• record_feedback MCP tool — agents write structured feedback (no external Slack/GitLab wiring).
• SQLite by default — a dropmcp.db file is created next to your content folders on first run.
• Postgres override — set DROPMCP_DATABASE_URL=postgresql://user:pass@host/db for durable hosted storage.
• Feedback UI — browse, search, filter, and triage at /feedback in the catalog SPA (GET/PATCH /api/feedback).

Feedback rows include feedback_type (correction by default, or agent_work), skill_name for the skill that was in use when feedback is about a specific skill, and optional structured details. agent_work entries can include reusable scripts or procedural artifacts under details.artifacts; script content is stored as JSON text and shown in an expandable UI panel.

SQLite auto-creates the feedback table and lightly adds missing skill_name, feedback_type, and details columns for existing local databases. Postgres deployments must ship the equivalent SyncDB migration for any missing columns:

ALTER TABLE feedback
  ADD COLUMN IF NOT EXISTS skill_name text,
  ADD COLUMN IF NOT EXISTS feedback_type text NOT NULL DEFAULT 'correction',
  ADD COLUMN IF NOT EXISTS details text;

COMMENT ON COLUMN feedback.skill_name IS 'Skill that was invoked or active when the feedback was produced; null when feedback is not about a specific skill.';
COMMENT ON COLUMN feedback.feedback_type IS 'Feedback category: correction for user corrections or agent_work for reusable work created after invoking a skill.';
COMMENT ON COLUMN feedback.details IS 'Optional JSON-encoded supporting material for agent_work feedback, such as reusable artifacts.';

Privacy guardrails: no verbatim user prompts, code, secrets, or PII. When feedback is enabled, dropmcp injects always-on guidance into the server instructions describing when and how agents should call record_feedback — no separate skill to install. Disable the whole feature (tool, HTTP routes, and instructions) with DROPMCP_FEEDBACK=false.

In containers, mount a volume over the SQLite file (or use Postgres) or feedback is lost when the pod restarts.

Per-user subscriptions

When DROPMCP_USER_SUBSCRIPTIONS=true, users can opt in to individual skills and prompts so their agent only sees a curated subset over MCP. The catalog UI still lists the full catalog; subscription checkboxes appear when the request includes the configured identity header (default X-User-Email, set upstream by your auth mesh).

• Flag off — unchanged behaviour; everything is published to every caller.
• Flag on, no identity header — MCP exposes everything; UI controls are disabled.
• Flag on, identity present, first sighting — user is logged in user_seen and automatically subscribed to every catalog group (MCP and HTTP). They can still opt out of individual items or whole groups afterwards.
• HTTP API — GET/POST /api/subscriptions, DELETE /api/subscriptions/{type}/{name}, group routes POST/DELETE /api/subscriptions/group/{group}, and POST /api/subscriptions/groups to re-subscribe every catalog group.
• group frontmatter — optional string on SKILL.md / PROMPT.md; surfaced in /catalog JSON and the catalog Group filter row. Group opt-ins are stored in user_group_subscription so new skills added to a followed group are included automatically; users can still opt out of individual items within a group via user_subscription_exclusion.
• SQLite auto-creates user_subscription, user_group_subscription, user_subscription_exclusion, and user_seen locally; Postgres consumers must ship a SyncDB migration (same caveat as feedback).

MCP clients cache tools/list / prompts/list — subscription changes take effect after the client re-lists (typically on reconnect).

E2E eval results (telemetry panel)

The catalog detail page includes an E2E Test Results panel (ported from skills-mcp) showing per-skill Promptfoo eval scores from your CI pipeline.

Eval results are pluggable — dropmcp ships the UI and HTTP routes, but the data source is optional so the library stays deployment-agnostic:

• Pass an eval_results_store to create_server() (any object implementing get_results_for_skill / get_all_latest_results), or

• Set DROPMCP_EVAL_RESULTS_PROJECT and install the StarRocks extra:

  pip install "dropmcp[starrocks]"
  export DROPMCP_EVAL_RESULTS_PROJECT="full-stack/agents/skills-mcp"
  export DROPMCP_EVAL_RESULTS_COMMIT_SHA="$(cat COMMIT_SHA)"
  

When no store is configured the panel renders an empty state; routes are not registered. This keeps StarRocks / Fleet JWT coupling out of the base package.

Skill and prompt format

SKILL.md

---
name: my-skill
category: my-category
group: my-group
description: One-line description shown to the LLM as the tool description.
instruction_summary: Short phrase for the server-level INSTRUCTIONS.md bullet.
---

Full skill body here — this is what the LLM receives when it calls the tool.

PROMPT.md

---
name: my-prompt
description: Short description shown in the catalog.
instruction_summary: Short phrase for INSTRUCTIONS.md.
arguments:
  - name: who
    description: The person to greet.
    required: true
  - name: tone
    description: Greeting tone (optional).
    required: false
---

Write a {{tone}} greeting addressed to {{who}}.

Validate your content before starting the server with the bundled checker:

python -c "import sys; from dropmcp.validate import run_validation; sys.exit(run_validation('skills', 'prompts'))"

Releasing

Releases are cut by pushing a v* git tag. The CI workflow does the rest: on a tag it builds the catalog UI, builds the wheel + sdist, publishes to PyPI via trusted publishing (no API token needed), and creates a GitHub Release with auto-generated notes.

To ship a new version:

1. Bump the version in both pyproject.toml (version) and src/dropmcp/__init__.py (__version__) — they must match, and the tag must match too. Use semver.

2. Land the bump on main via a merged PR (CI runs tests + the UI build on the PR).

3. Tag the merge commit and push the tag:

   git checkout main && git pull
   git tag v0.2.0
   git push origin v0.2.0
   

4. Watch the publish-pypi job in Actions. When it's green, the new version is live on PyPI and a GitHub Release exists for the tag.

The tag must start with v (e.g. v0.2.0) — that prefix is what gates the publish job. Pushing to main without a tag only runs tests and builds the wheel artifact; it never publishes.

License

Apache-2.0.