MCP ServerHTTPOfficialv1.16.0

Smithery Afgong Sqlite MCP Server

Query and explore SQLite databases by listing tables, inspecting schemas, and running SELECT queries through a simple MCP interface. Built for developers who need programmatic database inspection without writing SQL directly.

ai.smithery/afgong-sqlite-mcp-server

Hosted URL

https://server.smithery.ai/@afgong/sqlite-mcp-server/mcp

Transport

HTTP

Auth

No auth required

Smithery Afgong Sqlite repository at a glance

Live signal from GitHub, refreshed weekly.

Stars

0

Last commit

Oct 2, 2025

License

MIT

Language

Python

What the Smithery Afgong Sqlite MCP server does

How models use it and what it is built for.

Query and explore SQLite databases by listing tables, inspecting schemas, and running SELECT queries through a simple MCP interface. Built for developers who need programmatic database inspection without writing SQL directly.

Connect to Smithery Afgong Sqlite

Hosted endpoint — paste into any MCP client.

https://server.smithery.ai/@afgong/sqlite-mcp-server/mcp

Resources

Where to find authoritative docs and source for Smithery Afgong Sqlite.

Example prompts for Smithery Afgong Sqlite

Paste any of these into Agent Studio after connecting Smithery Afgong Sqlite.

  • What tables exist in my SQLite database?
  • Show me the schema and column types for the transactions table
  • Run a SELECT query to find all records where amount exceeds 5000
  • How many rows are in each table and what are their primary keys?

Documentation from project README

View on GitHub

Excerpted from the project's README — boilerplate sections (license, changelog, contributing) omitted for clarity.

Prerequisites

# Install dependencies
pip install -r requirements.txt

# Install FastMCP globally (if not already installed)
pip install fastmcp

COMMAND CHEATSHEET

# Run FastMCP directly for testing
SQLITE_DB_PATH=/Users/owner/claude-code/agentic-ai-learnings/hw3/sqlite-explorer-fastmcp-mcp-server/financial_data.db fastmcp run sqlite_explorer.py

# Test with inspector (if available)
SQLITE_DB_PATH=/Users/owner/claude-code/agentic-ai-learnings/hw3/sqlite-explorer-fastmcp-mcp-server/financial_data.db fastmcp inspect sqlite_explorer.py

# To install SQLite Explorer
SQLITE_DB_PATH=/Users/owner/claude-code/agentic-ai-learnings/hw3/sqlite-explorer-fastmcp-mcp-server/financial_data.db fastmcp install sqlite_explorer.py --name "SQLite Explorer"

# To launch SQLite Explorer via a web-based testing interface. Run with `--transport sse` for HTTP-based communication  
SQLITE_DB_PATH=/Users/owner/claude-code/agentic-ai-learnings/hw3/sqlite-explorer-fastmcp-mcp-server/financial_data.db fastmcp dev sqlite_explorer.py

# To set up the MCP server with Claude Desktop
SQLITE_DB_PATH=/Users/owner/claude-code/agentic-ai-learnings/hw3/sqlite-explorer-fastmcp-mcp-server/financial_data.db fastmcp claude-desktop add sqlite_explorer.py --name "SQLite Explorer"

# Need to define the SQLITE_DB_PATH variable before running smithery playground 
SQLITE_DB_PATH=/Users/owner/claude-code/agentic-ai-learnings/hw3/sqlite-explorer-fastmcp-mcp-server/financial_data.db smithery playground

After launching Smithery playground, we can now talk to the MCP server using this URL: https://smithery.ai/playground?mcp=https%3A%2F%2Fee09cd8f.ngrok.smithery.ai%2Fmcp

For VSCode with Cline

# Add this configuration to Cline MCP settings:
{
  "sqlite-explorer": {
    "command": "uv",
    "args": [
      "run",
      "--with",
      "fastmcp",
      "--with",
      "uvicorn",
      "fastmcp",
      "run",
      "/Users/owner/claude-code/agentic-ai-learnings/hw3/sqlite-explorer-fastmcp-mcp-server/sqlite_explorer.py"
    ],
    "env": {
      "SQLITE_DB_PATH": "/Users/owner/claude-code/agentic-ai-learnings/hw3/sqlite-explorer-fastmcp-mcp-server/financial_data.db"
    }
  }
}

Example output. MCP server provides four components. SQLite Explorer provides those tools.

Server Name: SQLite Explorer Generation: 2

Components Tools: 3 Prompts: 0 Resources: 0 Templates: 0

Environment FastMCP: 2.12.4 MCP: 1.15.0

This will open an interactive inspector where you can test the MCP tools:

  • list_tables - to see what tables are in your database
  • describe_table - to see the structure of a specific table
  • read_query - to run SELECT queries on your data

Notes

Even though we're running the MCP locally, still have a web interface For locally deployed MCP server SQLite Explorer, this is the MCP server URL that we can access as a client: http://localhost:6274/?MCP_PROXY_AUTH_TOKEN=e01acfa4461c1af5227669c2c0051e7cf9c280fb61a43a146a672463441f4c1b#tools In the future, once deployed, we'll have the same thing - HTTP or HTTPS, but instead of a local host, it'll be my server and the port. The rest will be something else. Local host is the server name, so the server is a local host. But if deployed somewhere, then it'll be a different host name. The port serves as a channel to talk to multiple MCP servers. HTTPS is a secure HTTP.

Sample Questions to Ask Your AI Assistant

Once the MCP server is connected, you can ask questions like:

  • "What tables are in my database?"
  • "Show me the structure of my financial data"
  • "What are the first 10 rows from [table_name]?"
  • "How many records are in each table?"
  • "Show me all transactions over $1000"
  • "What's the average amount in the transactions table?"

NOTE: The tables starting with SQLite are called system tables. We never created those tables.

Troubleshooting

# Check FastMCP version and available commands
fastmcp --help
fastmcp version

# Verify your database file exists
ls -la /Users/owner/claude-code/agentic-ai-learnings/hw3/sqlite-explorer-fastmcp-mcp-server/financial_data.db

# Test database directly with sqlite3
sqlite3 /Users/owner/claude-code/agentic-ai-learnings/hw3/sqlite-explorer-fastmcp-mcp-server/financial_data.db ".tables"

HW 3 Documentation

Step 1

Cloned this GitHub repository: https://github.com/hannesrudolph/sqlite-explorer-fastmcp-mcp-server

Step 2

Asked Claude Code: I was asked to evaluate the following deployment platforms: AWS, Vercel, Railway, and Smithery for my MCP server. I want the simplest and cheapest deployment. Which option should I select?

⏺ Based on your MCP server and requirements, I recommend Smithery.

Here's why:

Simplest: Smithery is specifically designed for MCP servers. It has the most straightforward deployment process for this exact use case.

Cheapest: Smithery offers a free tier for MCP server hosting, making it the most cost-effective option for your SQLite Explorer MCP server.

Other options comparison:

  • AWS: Most complex setup, requires managing infrastructure, not cost-effective for simple MCP servers
  • Vercel: Designed for web apps/APIs, not optimized for MCP servers, serverless model may not fit well
  • Railway: Good platform but more expensive than Smithery's free tier, more general-purpose

For a read-only SQLite MCP server like yours, Smithery's MCP-specific infrastructure is the best match for "simplest and cheapest."

Step 3

Tested this locally via Cline. In VS Code, we configured the system settings to launch a web-based interface with the following JSON file. See URL: http://localhost:6274/?MCP_PROXY_AUTH_TOKEN=a164e503687338cb23938baf05ae738ebe5cd0eaefa629e419cea7ef6ef51563#tools

Step 4

View the full README on GitHub

Smithery Afgong Sqlite MCP server — FAQ

Common questions about connecting and running Smithery Afgong Sqlite.

  • What does this MCP server do?

    SQLite Explorer provides three tools: list_tables (enumerate all tables), describe_table (inspect schema and columns), and read_query (execute SELECT statements). It lets you explore and query SQLite databases through an AI assistant without writing raw SQL.

  • How do I set it up?

    Install dependencies with `pip install -r requirements.txt` and FastMCP with `pip install fastmcp`, then set the `SQLITE_DB_PATH` environment variable to your database file and run `fastmcp run sqlite_explorer.py`. The README includes configurations for Claude Desktop and Cline in VSCode.

  • What deployment options are recommended?

    The README recommends Smithery for the simplest and cheapest deployment, as it offers a free tier and is specifically designed for MCP servers. Local testing via FastMCP dev mode or Claude Desktop integration is also supported.

  • Can I modify data or only read it?

    The README does not document write capabilities; the server appears to be read-only, supporting only SELECT queries and schema inspection. System tables (those starting with 'SQLite') are metadata and should not be queried.

  • What if my database connection fails?

    Verify the database file exists with `ls -la` on your `SQLITE_DB_PATH`, test it directly with `sqlite3 [path] ".tables"`, and check that FastMCP is installed with `fastmcp --help`. The README includes these troubleshooting steps.

Run Smithery Afgong Sqlite across 30+ AI models, side-by-side

Connect Smithery Afgong Sqlite to Claude, GPT, Gemini, DeepSeek and 30+ AI models in MCP Agent Studio. Compare answers side-by-side, save reusable agent presets, share runs — all in your browser, no install required.

Open Agent Studio

Related servers

More on MCP Playground