MCP server/ai-tools

MCP Builder MCP Server

MCP server with OpenAPI validation and analysis tools.

Charley-Forey-AI/mcp-builder-mcp ↗by Charley-Forey-AIupdated
1

Add it to Claude Code

claude mcp add mcp-builder -- uv run python main.py stdio
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Make your agent remember this setup

mcp-builder's config, env vars, and the gotchas you hit — recalled in every future Claude Code, Cursor, and Codex session.

npx conare@latest

Free · one command · indexes the sessions already on disk. Set up in the browser instead →

What it does

  • Validates OpenAPI specifications from local files, inline text, or public URLs
  • Fetches and parses remote OpenAPI content automatically
  • Generates developer context and inferred market segments for APIs
  • Creates specific agent prompts for developers building MCP tools

Tools 2

validate_openapiValidates and analyzes OpenAPI specifications provided via file path, inline JSON/YAML, or public URL.
prompt_mcp_developer_contextGenerates developer context, market segments, and agent prompts based on an OpenAPI specification.

Try it

Validate the OpenAPI specification located at this URL: https://example.com/api/openapi.json
Analyze my local OpenAPI file at ./api/spec.yaml and tell me if it is valid.
Generate developer context and agent prompts for the API specification I just validated.
Create a prompt for an agent that will help users interact with the API defined in this JSON string: { ... }
Original README from Charley-Forey-AI/mcp-builder-mcp

mcp-server-demo

MCP server with OpenAPI validation and analysis tools.

Running the server

Run: uv run python main.py

This starts the server on streamable-http at http://127.0.0.1:8000/mcp. It works locally and behind ngrok or other tunnels with no extra commands or env vars. For Cursor/stdio, use: uv run python main.py stdio.

The validate_openapi tool accepts file paths, inline JSON/YAML, or any public URL. The tool fetches and reads the content over HTTP (no manual download required). Supported URL types include: raw file URLs (e.g. raw.githubusercontent.com/.../ProjectSight-v1.json), Google Drive view or direct download links, and any other public URL that returns JSON or YAML. Both “read the page” and “download” semantics work: the tool GETs the URL and parses the response body.

After validation, the prompt_mcp_developer_context tool provides Trimble Agentic Ecosystem context, an inferred market segment and intended end users from the API, and two prompts for the agent creator: (1) the job role of the person adding MCP tools for this API, and (2) the task/workflow/job the agent being created will aid in. Pass either the analysis from validate_openapi (when valid) or openapi_input (URL/path/inline) to get these prompts.

Frequently Asked Questions

What are the key features of MCP Builder?

Validates OpenAPI specifications from local files, inline text, or public URLs. Fetches and parses remote OpenAPI content automatically. Generates developer context and inferred market segments for APIs. Creates specific agent prompts for developers building MCP tools.

What can I use MCP Builder for?

Validating API documentation before building an MCP server. Generating boilerplate prompts for AI agents to interact with a specific API. Analyzing existing APIs to determine the best way to expose them as MCP tools.

How do I install MCP Builder?

Install MCP Builder by running: uv run python main.py stdio

What MCP clients work with MCP Builder?

MCP Builder works with any MCP-compatible client including Claude Desktop, Claude Code, Cursor, and other editors with MCP support.

Conare · memory for coding agents

Turn this server into reusable context

Keep MCP Builder docs, env vars, and workflow notes in Conare so your agent carries them across sessions.

Set up free$npx conare@latest