MCP Server Generator
Generate a working Model Context Protocol server in TypeScript or Python — 8 prebuilt integrations or describe your own. Free, no signup.
Server spec
Pick an integration and capabilities — Gemini writes the rest.
Query a Postgres database — SELECT, INSERT, UPDATE, DELETE, schema introspection.
Standard I/O — for desktop apps (Claude Desktop, Cursor, Windsurf).
Free, 5 generations / day per IP.
Configure your server, then click Generate
We'll write a complete, production-grade MCP server in TypeScript or Python — server setup, tool definitions, handlers, and error handling included.
How the generator works
- 1
Pick language and integration
Choose TypeScript or Python, then select an integration from the eight presets — or pick "Custom" and describe your own data source.
- 2
Confirm capabilities and transport
Each preset auto-selects sensible defaults (read, write, auth-required, etc.). Tweak the checkboxes, pick stdio (desktop) or SSE (remote), and name your server.
- 3
Generate the code
Click Generate. Gemini writes a complete server file with 3+ working tools, input validation, error handling, and a README in 8–15 seconds.
- 4
Wire it into Claude Desktop, Cursor, or Windsurf
The "How to use" tab gives you the exact claude_desktop_config.json snippet. Drop the file in, set env vars, restart your client — your tools are live.
Frequently asked questions
What is an MCP server, and why would I build one?
Model Context Protocol (MCP) is Anthropic's open standard for connecting AI assistants (Claude Desktop, Cursor, Windsurf, Zed) to external data sources and tools. An MCP server exposes a list of "tools" — each with a JSON-schema input — that the assistant can call. You build one when you want Claude (or any MCP-aware client) to read your Postgres database, post to Slack, query Notion, manage GitHub issues, etc., directly from a chat. The protocol handles transport, discovery, and invocation; you just write the tool handlers.
How is the generated code structured?
For TypeScript: a single ESM file using @modelcontextprotocol/sdk — Server instance, ListToolsRequestSchema + CallToolRequestSchema handlers, StdioServerTransport (or SSE) connect. For Python: a single asyncio file using the official mcp package — Server, @list_tools and @call_tool decorators, stdio_server transport. Both versions include real implementation logic per tool, runtime input validation, structured error responses, and environment-variable checks at startup. No "// TODO" stubs.
Which integrations are supported out of the box?
Eight prebuilt presets cover the most-requested data sources: PostgreSQL, Slack, Notion, GitHub, Google Sheets, Stripe, HubSpot, and Airtable. Each preset auto-selects a sensible default capability set (read/write/auth-required, with subscribe added for event-emitting APIs). The "Custom integration" mode takes a free-form description — useful for internal REST APIs, file systems, or anything not on the list.
How do I wire the generated server into Claude Desktop?
After generation, the "How to use" tab gives you the exact claude_desktop_config.json snippet. Add it under the mcpServers key in ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows), set the env vars listed in the README, then quit and relaunch Claude Desktop. Cursor, Windsurf, and Zed use the same mcpServers schema with their own config-file paths.
Is the generated code production-ready?
It's a solid scaffold — typed, with input validation, structured error handling, and env-var checks. For local dev or single-user tools, you can ship as-is. For multi-tenant production deployments, audit the auth path, add rate limits, persistent logging, and tighten the JSON-schema validation per tool. We always recommend reviewing the generated code before pointing it at production data.
Why Gemini instead of Claude for the code-gen?
Gemini 2.0 Flash gives us reliable JSON-mode output, generous free-tier quota, and 8K-token responses big enough for non-trivial servers. The system prompt frames the model as a senior MCP engineer and feeds it our reference structure for both languages. Output quality is consistent — when it occasionally drifts (placeholder comments, fence-wrapped code), the route handler cleans it up before returning to the browser.
Need a production-grade MCP deployment?
We build MCP servers, AI agents, and end-to-end automation for teams across India and worldwide. Multi-tenant auth, audit logging, schema versioning, monitoring — the things you need before pointing an LLM at production data. The same engineers who built TalkDrill, our in-house English-speaking app, ship enterprise AI automation for clients.
Talk to our AI-automation teamNeed a custom MCP server or AI integration?
We design and ship Model Context Protocol servers, AI agents, and bespoke automations for teams that need more than a scaffold.