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urjeetpatel

db-tools-mcp

by urjeetpatel
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Python
Remote

db-tools-mcp

MCP server that exposes SQL Server and Snowflake schema metadata to AI coding agents. It caches table/column/FK information locally and provides tools for searching schemas, finding join paths, and managing database connections — all without running live queries on every request.

Quick start

1. Install and run with uvx

uvx db-tools-mcp

Or install with pip:

pip install db-tools-mcp
db-tools-mcp

Or install locally for development:

git clone https://github.com/urjeetpatel/db-tools-mcp.git
cd db-tools-mcp
uv sync
uv run db-tools-mcp

2. Register with your MCP client

Claude Desktop (claude_desktop_config.json):

{
  "mcpServers": {
    "db-tools": {
      "command": "uvx",
      "args": ["db-tools-mcp"]
    }
  }
}

Claude Code (.mcp.json in your project root):

{
  "mcpServers": {
    "Db_Tools": {
      "command": "uvx",
      "args": ["db-tools-mcp"]
    }
  }
}

3. Add your first database

Use the add_database tool through your MCP client:

add_database(
  name="my_db",
  db_type="sqlserver",
  url="mssql+pyodbc:///?odbc_connect=DRIVER=ODBC Driver 17 for SQL Server;Server=myhost;Database=MyDB;Trusted_Connection=Yes;"
)

Or copy the example config manually:

# Linux / macOS
mkdir -p ~/.config/db-tools
cp config.example.yaml ~/.config/db-tools/config.yaml

# Windows (PowerShell)
New-Item -ItemType Directory -Force -Path "$env:USERPROFILE\.config\db-tools"
Copy-Item config.example.yaml "$env:USERPROFILE\.config\db-tools\config.yaml"

Then edit ~/.config/db-tools/config.yaml with your connection details.

4. Populate the cache

refresh_metadata()          # all sources
refresh_metadata(source="my_db")  # one source

Or from the command line:

uvx db-tools-refresh
db-tools-refresh --source my_db   # if installed locally

Related MCP server: RunContext

Tools

Read tools (safe, use cached data)

Tool

Description

list_sources()

List cached database sources

list_schemas(source)

List schemas in a source

list_tables(source, schema)

List tables in a schema

get_table(source, schema, table)

Get columns + FK relationships

get_dialect(source)

SQL dialect (mssql, snowflake)

list_all_foreign_keys(source, schema)

All FKs in a schema

find_direct_joins(source, table_a, table_b)

FK joins between two tables

suggest_joins(source, table_a, table_b)

Multi-hop join path suggestions

search_tables(source, keyword)

Search table names

search_columns(source, column_name)

Search column names

Stored procedure tools

Tool

Description

list_stored_procedures(source, schema)

List stored procedure names in a schema

get_stored_procedure(source, schema, name)

Get SP metadata: parameters, dates, and definition

search_stored_procedures(source, keyword)

Search SP names (case-insensitive, optional schema filter)

search_stored_procedure_text(source, keyword)

Search SP body text for a keyword; returns matching procedures with a one-line excerpt

get_call_template(source, schema, name, style)

Generate a SQL or Python call template for an SP

export_stored_procedure(source, schema, name, output_file)

Write SP definition (SQL only) to a file; returns resolved path + line count

export_stored_procedure writes the raw SQL definition only — no JSON wrapper. The output_file must be an absolute path to a writable location; writes to system directories, network paths, drive roots, and the db-tools config directory are blocked.

Admin tools (require confirmation)

Tool

Description

add_database(name, db_type, ...)

Add a source to config + test connection

refresh_metadata(source, force)

Re-scan live databases (throttled to 1/day)

Configuration

Config and cache live in ~/.config/db-tools/ (XDG standard):

~/.config/db-tools/
  config.yaml          # database connections
  metadata_cache/      # cached JSON per source
  .refresh_state.json  # last-refresh timestamps
  server.log           # MCP server logs

Override the location with DB_TOOLS_CONFIG_DIR or XDG_CONFIG_HOME:

DB_TOOLS_CONFIG_DIR=/custom/path db-tools-mcp

Supported source types

SQL Server (direct ODBC):

my_db:
  enabled: true
  url: "mssql+pyodbc:///?odbc_connect=DRIVER=ODBC Driver 17 for SQL Server;Server=host;Database=db;Trusted_Connection=Yes;"
  include_schemas: ["*"]
  exclude_schemas: [INFORMATION_SCHEMA, sys, db_owner, ...]

Snowflake (via SQL Server linked server / OPENQUERY):

my_snowflake:
  enabled: true
  sqlserver_url: "mssql+pyodbc:///?odbc_connect=..."
  linked_server: "SNOWFLAKE"
  database: "MY_SNOWFLAKE_DB"
  include_schemas: ["*"]
  exclude_schemas: [INFORMATION_SCHEMA]

Requirements

  • Python >= 3.11

  • ODBC Driver 17 for SQL Server (for SQL Server and Snowflake-via-linked-server connections)

  • Network access to the target databases

License

MIT

Install Server
A
license - permissive license
A
quality
B
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
–Release cycle
–Releases (12mo)
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