MCPMarket
Sell SkillsPower Your AgentsConnect
  1. Home
  2. Servers
  3. Model Control Protocol Server

Model Control Protocol Server

TomasRodriguez2002byTomasRodriguez2002
•
API Development
Other
Deployment & DevOps

Integrates the Model Control Protocol (MCP) with OpenAI's API, enabling OpenAI to access and use tools exposed by an MCP server.

Related MCPs

View more
  • neondatabase-labs

    Neon

    Enables natural language interaction with the Neon Management API and databases through the Model Context Protocol.

  • mendableai

    Firecrawl

    Empowers LLMs with advanced web scraping capabilities for content extraction, crawling, and search functionalities.

  • GLips

    Figma Context

    Provides AI coding agents with simplified Figma layout information via the Model Context Protocol.

Related Skills

View all
  • openclaw

    Diagram Maker & Visualizer

    Generates professional SVG, HTML, and Excalidraw diagrams for software architecture, system flows, and educational concepts.

  • openclaw

    GH Issues Auto-Fixer

    Automates the end-to-end GitHub issue lifecycle by spawning sub-agents to implement code fixes, open pull requests, and resolve review comments.

  • openclaw

    Discord Integration

    Manages Discord operations including messaging, reactions, and channel management directly through Claude.

MCPMarket

Discover MCP servers that connect MCP clients like Claude and Cursor to your favorite tools. Browse the MCP Market to get started.

Browse

  • MCP Search
  • MCP Servers
  • MCP Clients
  • Agent Skills
  • MCP Market Hub
  • Categories
  • What is an MCP server?
  • Model Context Protocol

Rankings

  • Top MCPs Today
  • Top Agent Skills Today
  • Top 100 Agent Skills
  • Top 100 MCP Servers

About

  • News
  • Submit
  • Contact

© 2026 MCP Market. All rights reserved.·Privacy·Terms

This project provides a demonstration of integrating the Model Control Protocol (MCP) with the OpenAI API. It allows OpenAI to interact with and utilize tools exposed by an MCP server running in Docker. The setup includes an MCP server implementation, a client for connecting and calling the agent, and a Dockerfile for easy deployment. The data flow involves user queries, OpenAI tool selection, communication between the MCP client and server, and the generation of a final response incorporating tool data.

Key Features

01Client for connecting to the server and calling the agent
02MCP server implementation with a tool
03Dockerized deployment for easy setup
04SSE (Server-Sent Events) transport
050 GitHub stars
06Integration with OpenAI API

Use Cases

01Allowing OpenAI to utilize external tools
02Facilitating communication between OpenAI and MCP servers
03Enabling OpenAI to access knowledge base data