snowsyncmd-mcp 1.2.0

MCP server for the SnowSyncMD Snowflake Native App — exposes schema docs, structured object search, column inspection, real-time annotations, and safe read-only SQL queries as Claude tools

SnowSyncMD MCP Server

Connects Claude Code, Cursor, and any MCP-compatible AI assistant directly to your SnowSyncMD Native App so Claude can read Snowflake schema documentation automatically — no copy-pasting, no manual downloads, no live INFORMATION_SCHEMA queries.

How it works

You ask Claude: "Write a query joining ORDERS to CUSTOMERS"
       ↓
Claude calls: snowflake_search_objects("orders")   → finds ORDERS, FACT_ORDERS, …
Claude calls: snowflake_get_schema("MY_DB","SALES","ORDERS")
Claude calls: snowflake_get_schema("MY_DB","SALES","CUSTOMERS")
       ↓
Claude gets the column list from pre-built Markdown docs (no warehouse spin-up)
       ↓
Claude writes the correct query with real column names and types

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Installation

pip install snowsyncmd-mcp

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Configuration

Add to your Claude Code settings (~/.claude/settings.json):

{
  "mcpServers": {
    "snowsyncmd": {
      "command": "snowsyncmd-mcp",
      "env": {
        "SNOWFLAKE_ACCOUNT":   "your-account-identifier",
        "SNOWFLAKE_USER":      "your_username",
        "SNOWFLAKE_PASSWORD":  "your_password",
        "SNOWFLAKE_ROLE":      "ACCOUNTADMIN",
        "SNOWFLAKE_WAREHOUSE": "COMPUTE_WH",
        "SNOWSYNCMD_APP":      "snowsyncmd"
      }
    }
  }
}

Tip: Put credentials in a .env file at the project root instead of hardcoding them in settings.json.

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Available tools

Claude sees these 9 tools and calls them automatically — no prompting required.

snowflake_get_schema

Returns the full Markdown schema documentation for one Snowflake object. Covers tables, views, functions, procedures, stages, pipes, tasks, masking policies, dynamic tables, and more. Each doc includes column names, data types, nullability, defaults, business descriptions, and a generation timestamp.

When Claude uses it: Before writing SQL, before explaining table relationships, before reviewing any query that touches a specific object.

Claude: [calls snowflake_get_schema("SALES_DB","CORE","FACT_ORDERS")]
→ Returns 408-token Markdown doc with all 14 columns, types, and comments

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snowflake_search_schema

Keyword search across all tracked object names using the stage file index. Returns a list of matching fully-qualified names.

When Claude uses it: When the user mentions a table by a partial name or concept and Claude needs to find the exact DB.SCHEMA.OBJECT path before calling get_schema.

Claude: [calls snowflake_search_schema("revenue")]
→ Returns: SALES_DB.CORE.FACT_ORDERS, SALES_DB.REPORTING.V_DAILY_REVENUE

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snowflake_search_objects (v1.1.0)

Structured search against the snowflake_objects_ref reference table. Supports keyword match on object name, description, and business context; filter by database; filter by object type. Returns enriched results including owner, PII flag, business context, SLA description, and tags alongside the name.

When Claude uses it: Exploring unfamiliar databases, finding all tables in a domain, discovering who owns an object, checking PII status before generating SQL.

Claude: [calls snowflake_search_objects(query="customer", object_type="BASE TABLE")]
→ Returns: CUSTOMERS [owner=data_team, pii_flag=true, business_context="..."]

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snowflake_get_columns (v1.1.0)

Returns the structured column list for a specific table or view directly from snowflake_columns_ref — without loading the full Markdown schema doc. Each column entry includes name, data type, nullability, default, and business description.

When Claude uses it: Quick column inspection, checking whether a specific column exists, counting columns, finding nullable columns — when the full MD doc isn't needed.

Claude: [calls snowflake_get_columns("SALES_DB","CORE","FACT_ORDERS")]
→ Returns: 14 columns with types and nullability in a tabular format

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snowflake_query (v1.2.0)

Execute a read-only SQL query against Snowflake directly from a Claude conversation. The SQL classifier — running in the MCP Python client under the caller's own credentials — blocks every mutating statement before it reaches Snowflake. Allowed statement types: SELECT, SHOW, DESCRIBE, and EXPLAIN. Multi-statement injection (semicolon-separated) is also blocked. Results are capped at 1000 rows. Every call (blocked or allowed) is written to core.query_audit_log in the Native App.

Security design:

• Classification runs in Python (not in Snowflake), so queries execute under the caller's own role
• Masking policies are applied correctly — PII stays masked for restricted roles
• Blocked keywords include: INSERT, UPDATE, DELETE, MERGE, CREATE, DROP, ALTER, TRUNCATE, GRANT, REVOKE, COPY, PUT, GET, EXECUTE, CALL, SET, and more

When Claude uses it: Row counts, data samples, DISTINCT value checks, NULL checks, ad-hoc exploration — whenever the user asks to inspect actual data rather than schema.

You:    "How many orders were placed last month?"
Claude: [calls snowflake_query("SELECT COUNT(*) FROM FACT_ORDERS WHERE ORDER_DATE >= DATEADD('month',-1,CURRENT_DATE())")]
→ Returns: 1 row — COUNT(*): 14,832

You:    "Show me a sample of the CUSTOMERS table"
Claude: [calls snowflake_query("SELECT * FROM SALES.CORE.CUSTOMERS LIMIT 5")]
→ Returns: 5 rows with masked PII (masking policies applied for caller's role)

You:    "DELETE FROM customers WHERE id=1"
Claude: [calls snowflake_query("DELETE FROM customers WHERE id=1")]
→ Query blocked — Statement type "DELETE" is not allowed. Block logged to audit trail.

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snowflake_annotate (v1.1.0)

Writes a team annotation to any tracked Snowflake object. Changes are stored in snowflake_objects_ref and are immediately visible to all users — no sync cycle needed. Supports five enrichment fields:

Field	Type	Example
owner	text	"data_team"
business_context	text	"Core revenue table, source of truth for P&L"
sla_description	text	"Refreshed hourly, 99.9% availability SLA"
pii_flag	boolean	"true" or "false"
tags	JSON	'{"domain":"finance","cost_centre":"data"}'

When Claude uses it: When a user says "mark this table as PII", "set the owner of FACT_ORDERS to data_team", or "add a description to this view".

You:    "Mark CUSTOMERS as PII and set the owner to data_team"
Claude: [calls snowflake_annotate("SALES_DB","CORE","CUSTOMERS","pii_flag","true")]
Claude: [calls snowflake_annotate("SALES_DB","CORE","CUSTOMERS","owner","data_team")]
→ Saved. Visible to all users instantly.

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snowflake_list_objects

Lists every tracked object across all registered databases with database, schema, type, and name. Optionally filtered by database or object type.

When Claude uses it: Getting an overview of what's available before starting a task, counting objects by type, confirming that a database has been registered.

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snowflake_get_status

Returns SnowSyncMD sync health: task state, registered databases with priorities and object counts, total objects tracked, MD files present, and last sync timestamp.

When Claude uses it: When the user asks if the schema docs are up to date, or after a sync is triggered to confirm it completed.

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snowflake_sync

Triggers an immediate sync cycle for all registered databases or one specific database. Returns objects scanned, changed, and MD files written.

When Claude uses it: After the user creates new tables or alters schema and asks Claude to "refresh" or "update" the documentation.

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What's new in 1.2.0

Tool	1.1.x	1.2.0
snowflake_get_schema	Returns full MD doc for one object	Unchanged
snowflake_search_schema	Keyword search on object names (stage-based)	Unchanged
snowflake_list_objects	List all objects with DB/schema/type	Unchanged
snowflake_get_status	Sync health, registered databases, last sync	Unchanged
snowflake_sync	Trigger immediate sync for all or one DB	Unchanged
snowflake_search_objects	Structured search with enrichment fields	Unchanged
snowflake_get_columns	Structured column list from ref table	Unchanged
snowflake_annotate	Real-time object annotations	Unchanged
snowflake_query	—	New — read-only SQL query runner with client-side SQL classifier; blocks all mutating statements; applies caller's masking policies; row cap 1,000; full audit log

Backend changes in 1.2.0 (requires SnowSyncMD Native App re-deploy)

• core.query_audit_log — new table: every query attempt logged (blocked or allowed) with keyword, status, row count, duration, and error
• api.log_safe_query — new stored procedure: audit sink called by the MCP client after each query; uses EXECUTE AS OWNER (Native App framework requirement)
• SQL classification runs client-side in Python so queries execute under the caller's own role — masking policies apply correctly

What's new in 1.1.0

Tool	1.0.0	1.1.0
snowflake_get_schema	Returns full MD doc for one object	Unchanged
snowflake_search_schema	Keyword search on object names (stage-based)	Unchanged
snowflake_list_objects	List all objects with DB/schema/type	Unchanged
snowflake_get_status	Sync health, registered databases, last sync	Unchanged
snowflake_sync	Trigger immediate sync for all or one DB	Unchanged
snowflake_search_objects	—	New — structured search across snowflake_objects_ref; filters by keyword, database, and object type; returns owner, PII flag, business context, and tags alongside names
snowflake_get_columns	—	New — structured column list from snowflake_columns_ref; returns column name, type, nullability, default, and description without loading the full MD doc
snowflake_annotate	—	New — write owner, business context, SLA, PII flag, or JSON tags to any object; changes are real-time (no sync required) and immediately visible to all users

Backend changes in 1.1.0 (requires SnowSyncMD Native App re-deploy)

• core.snowflake_objects_ref — new table: one row per tracked object with full metadata + enrichment fields
• core.snowflake_columns_ref — new table: one row per column across all tables and views (1,500+ rows for typical accounts)
• api.search_objects — new stored procedure backing snowflake_search_objects
• api.list_columns — new stored procedure backing snowflake_get_columns
• api.update_enrichment — new stored procedure backing snowflake_annotate
• generate_md — updated to MERGE into both ref tables after every MD file write

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Example conversations

You:    "What columns does FACT_ORDERS have?"
Claude: [calls snowflake_get_schema("SALES","CORE","FACT_ORDERS")]
Claude: "FACT_ORDERS has 14 columns: ORDER_SK (NUMBER, NOT NULL), ORDER_DATE (DATE), ..."

You:    "Find all tables related to customers and tell me which ones have PII"
Claude: [calls snowflake_search_objects("customer", object_type="BASE TABLE")]
Claude: "Found 4 customer tables. CUSTOMERS and CUSTOMER_PII_LOG are flagged as PII."

You:    "Show me the columns of the ORDERS table quickly"
Claude: [calls snowflake_get_columns("SALES","CORE","ORDERS")]
Claude: "ORDERS has 9 columns: ORDER_ID (NUMBER, NOT NULL), CUSTOMER_ID (NUMBER), ..."

You:    "Mark FACT_ORDERS as owned by the data team"
Claude: [calls snowflake_annotate("SALES","CORE","FACT_ORDERS","owner","data_team")]
Claude: "Done. FACT_ORDERS is now annotated with owner=data_team."

You:    "Write a query to show monthly revenue by channel"
Claude: [calls snowflake_search_objects("revenue")]
Claude: [calls snowflake_get_schema for top result]
Claude: "Here's a query using V_DAILY_REVENUE which already aggregates by channel: ..."

You:    "Is the schema documentation up to date?"
Claude: [calls snowflake_get_status]
Claude: "Last synced 3 minutes ago. 191 objects tracked across 2 databases."

You:    "I just added a new table — refresh the docs"
Claude: [calls snowflake_sync]
Claude: "Sync complete. 1 new object found and documented."

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Upgrading from 1.0.0

pip install --upgrade snowsyncmd-mcp

Then re-deploy the SnowSyncMD Native App to get the new ref tables and procedures:

snow app run --connection snowsyncmd_deploy

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Requirements

• SnowSyncMD Native App v1.1+ installed in your Snowflake account
• ACCOUNTADMIN or app_admin role on the SnowSyncMD app
• Python 3.11+
• Dependencies installed automatically: mcp, snowflake-connector-python, python-dotenv