prayanks

SQLite MCP Server

Community prayanks
Updated

These are MCP server implementations for accessing a SQLite database in your MCP client. There is both a SDIO and a SSE implementation.

SQLite MCP Server

This repository contains an MCP (Model Context Protocol) server written in Python that connects to a SQLite database containing startup funding data. The server exposes table schemas as resources, provides a read-only SQL query tool, and offers prompt templates for common data analysis tasks. It is designed to work with MCP clients and language models (LLMs) and communicates via the STDIO protocol.

Table of Contents

  • Overview
  • Features
  • Setup and Installation
    • Creating the Sample SQLite Database
    • Creating a Virtual Environment
    • Running the MCP Server
    • Installing into Claude Desktop
  • Usage
  • Testing
  • Logging
  • License

Overview

The MCP server uses the MCP Python SDK (with CLI extras) to implement a server that:

  • Connects to a SQLite database (e.g., a database with startup funding information).
  • Exposes table schemas as MCP resources.
  • Provides a tool for executing read-only SQL queries.
  • Offers prompt templates that help language models generate data analysis insights.
  • Communicates via the STDIO protocol, reading JSON-RPC messages from standard input and writing responses to standard output.

Features

  • Resources

    • schema://sqlite/{table}: Returns the SQL schema for a specific table.
    • schema://sqlite/all: Returns a JSON mapping of all table schemas.
  • Tools

    • sql_query: Executes read-only SQL queries. Only SELECT statements are permitted.
  • Prompts

    • analyze_table_prompt: Generates an analysis prompt for a specific table.
    • describe_query_prompt: Generates a prompt explaining a SQL query.
  • STDIO Protocol

    • Reads from stdin and writes responses to stdout, making integration easy.
  • Logging

    • Uses Python’s logging module to trace activity and debug errors.

Setup and Installation

Creating the Sample SQLite Database

Save the following script as create_db.py:

<same as earlier>

Run with:

python create_db.py

Creating a Virtual Environment

python -m venv venv

Activate the environment:

  • macOS/Linux:

    source venv/bin/activate
    
  • Windows:

    venv\Scripts\activate
    

Install dependencies:

pip install "mcp[cli]"

Running the MCP Server

  1. Save your server code as sqlite_mcp_server.py.
  2. Run the server:
python sqlite_mcp_server.py

Optional (using uv):

uv run sqlite_mcp_server.py

Installing into Claude Desktop

Save the following as install_to_claude.py and run it:

python install_to_claude.py

Update Claude Desktop config with:

{
  "mcpServers": {
    "sqlite_mcp_server": {
      "command": "python",
      "args": ["-u", "/absolute/path/to/sqlite_mcp_server.py"]
    }
  }
}

Restart Claude Desktop afterward.

Usage

  • Access Resources: schema://sqlite/all, schema://sqlite/startups
  • Invoke Tools:
    SELECT * FROM startups WHERE funding_amount > 10000000;
    
  • Use Prompts: Generate SQL or explain queries.

Testing

Run the test script:

python sqlite_mcp_client_tests.py sqlite_mcp_server.py

This script tests:

  • Listing resources
  • Retrieving schemas
  • Valid/invalid queries
  • Prompt templates

Logging

Modify logging config:

logging.basicConfig(
    filename='mcp_server.log',
    level=logging.DEBUG,
    format="[%(asctime)s] %(levelname)s - %(message)s",
    datefmt="%Y-%m-%d %H:%M:%S"
)

Logs print to stderr by default.

License

This project is licensed under the MIT License. See the LICENSE file.

Additional Notes

  • Adjust paths in installation/config scripts as needed.
  • Ensure the SQLite database is created before running the server.
  • Integrates with MCP Inspector or Claude Desktop.

MCP Server · Populars

MCP Server · New

    kuberstar

    Qartez MCP

    Semantic code intelligence MCP server for Claude Code - project maps, symbol search, impact analysis, and more

    Community kuberstar
    aovestdipaperino

    tokensave

    Rust port of CodeGraph — a local-first code intelligence system that builds semantic knowledge graphs from codebases. Ported from the original TypeScript implementation by @colbymchenry.

    Community aovestdipaperino
    jpicklyk

    MCP Task Orchestrator

    Server-enforced workflow discipline for AI agents. An MCP server providing persistent work items, dependency graphs, quality gates, and actor attribution. Schemas define what agents must produce — the server blocks the call if they don't. Works with any MCP-compatible client.

    Community jpicklyk
    AgentsID-dev

    AgentsID Scanner

    Security scanner for MCP servers. Grades auth, permissions, injection risks, and tool safety. The Lighthouse of agent security.

    Community AgentsID-dev
    remete618

    widemem.ai

    Next-gen AI memory layer with importance scoring, temporal decay, hierarchical memory, and YMYL prioritization

    Community remete618