What is the HTTP Client (MCP HTTP Client)?

The HTTP Client is an MCP server that empowers AI assistants to execute HTTP/HTTPS requests, GraphQL queries, and raw TCP/Telnet connections using natural language commands.

How does the HTTP Client improve performance?

It features an intelligent LRU cache with TTL for GET requests, providing a significant 13.7x speedup on repeated requests by serving them from cache, balancing performance with data freshness.

What types of requests does this tool support?

It supports all HTTP/HTTPS methods (GET, POST, PUT, DELETE, PATCH, HEAD, OPTIONS), GraphQL queries with variables and headers, and raw TCP/Telnet connections for network testing.

How can I integrate this tool with an AI assistant?

As an MCP server, it can be easily configured with AI assistants like Cursor and Claude Desktop by adding its JAR path to their `mcpServers` configuration.

HTTP Client

Name: HTTP Client
Author: ferPrieto

byferPrieto

•

API Development

Developer Tools

Productivity & Workflow

Facilitates making HTTP requests, GraphQL queries, and raw TCP/Telnet connections using natural language from AI assistants.

This Model Context Protocol (MCP) server empowers AI assistants to seamlessly interact with external web services and network protocols using natural language commands. It acts as a bridge, allowing AI to perform a wide array of tasks, from fetching data via HTTP, executing complex GraphQL queries, to establishing raw TCP connections, all while simplifying the underlying complexities of these network operations and enhancing performance through intelligent caching.

Key Features

01Raw TCP/Telnet connections for network testing and message sending

02Natural language parsing for command input from AI assistants

03Intelligent LRU caching with TTL for significant performance improvements on repeated GET requests

04Full HTTP/HTTPS request support (GET, POST, PUT, DELETE, PATCH, HEAD, OPTIONS)

051 GitHub stars

06GraphQL query execution with variables, headers, and operation names

Use Cases

01Enabling AI assistants to interact with and retrieve data from web APIs

02Automating complex API calls and GraphQL queries through natural language commands

03Performing basic network connectivity tests and sending raw TCP messages