Loading...
Loading...
Generate a working Model Context Protocol server in TypeScript or Python — 8 prebuilt integrations or describe your own. Free, no signup.
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.
We'll write a complete, production-grade MCP server in TypeScript or Python — server setup, tool definitions, handlers, and error handling included.
Choose TypeScript or Python, then select an integration from the eight presets — or pick "Custom" and describe your own data source.
Each preset auto-selects sensible defaults (read, write, auth-required, etc.). Tweak the checkboxes, pick stdio (desktop) or SSE (remote), and name your server.
Click Generate. Gemini writes a complete server file with 3+ working tools, input validation, error handling, and a README in 8–15 seconds.
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.
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.
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.
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.
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.
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.
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.
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 our in-house edtech (PenLeap) and English-speaking app (TalkDrill) ship enterprise AI automation for clients.
Talk to our AI-automation teamWe design and ship Model Context Protocol servers, AI agents, and bespoke automations for teams that need more than a scaffold.