SQLite Explorer

SQLite Explorer

MCP server for exploring SQLite databases, offering tools to list tables, describe table structures, and execute read-only SQL queries.

Category
访问服务器

README

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

smithery playground URL : https://smithery.ai/playground?mcp=https%3A%2F%2F143c4151.ngrok.smithery.ai%2Fmcp

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选