"Automating HTTP API Wrapping into MCPT Tool" matching MCP servers:

Wraps bare-IP HTTP REST APIs as MCP tools, supporting dynamic IP switching and stdio/SSE modes for use in AI IDEs.

Semantic navigator for MCP tools that finds relevant tools by natural language intent, reducing token usage by 95%.

Provides an HTTP API for Claude Code using a native MCP channel to enable stable message exchange without terminal screen-scraping. It allows users to programmatically send messages, retrieve conversation history, and stream real-time events via standard web protocols.

A Model Context Protocol server that provides a scratchpad tool for LLMs to organize thoughts, take notes, and plan approaches to complex problems.

MCP router that fronts your other MCP servers behind a few small generic tools, so their tool definitions stay out of the context window and are discovered on demand.

A tool discovery MCP server that integrates with Azure DevOps wikis and code-graph-rag to match natural language goals to tools, enabling tool composition and proxy testing.

Stateful interactive HTTP REPL and Agentic MCP engine for API navigation and testing.

Enables MCP clients to interact with OpenCode 2.x/3.x servers through direct /api/... HTTP routing and ~85 tools spanning sessions, messages, files, config, providers, projects, input, TUI, and workflows. Runs as a zero-dependency, pure-JavaScript stdio bridge with no SDK or build step.

A server built on mcp-framework that enables integration with Claude Desktop through the Model Context Protocol.

A Node.js proxy that enables communication between stdio-based clients and HTTP-based Model Context Protocol servers. It facilitates protocol translation, session management, and authentication to allow remote HTTP servers to function as local stdio servers.

Enables MCP clients to connect over HTTP/SSE, call simple echo, arithmetic, uppercasing, and time tools, and run asynchronous tasks with approval elicitations via the Tasks extension.

An MCP server that provides HTTP client capabilities with support for full HTTP methods, custom headers, query parameters, request bodies, and configurable timeouts.

Mock HTTP server implementing MCP (Model Context Protocol) with streamable HTTP transport, health endpoint, and example tools like echo and time.

An HTTP-based Model Context Protocol server for RiotPlan that exposes comprehensive planning tools, resources, and prompts for AI agents. It enables full plan lifecycle management, including ideation, shaping, execution tracking, and retrospective generation.

Enables clients to interact with a spec-shaped Model Context Protocol endpoint over Streamable HTTP, supporting initialization, session handling, tool listing and calls, plus ping, with origin validation and protocol version enforcement.

Provides a unified MCP server to interact with multiple middleware (Loki, MySQL, Redis, RocketMQ, Elasticsearch, MongoDB) through a desktop application for connection management and configuration export.

Turns any OpenAPI spec into a single-tool MCP server where the model writes JavaScript against a typed api object in a QuickJS sandbox, so multi-step tasks run in one round trip and only the final result reaches context. Credentials, rate limiting, retries, write gating and audit logging stay on the host, outside the sandbox.

Enables converting documents between formats over MCP: any supported input (docx, odt, xlsx, ods, pptx, odp, pdf) to structured Markdown, Markdown to docx, odt or pdf, direct cross-format pairs, and Markdown to sanitised HTML. Fidelity losses are reported rather than failing the conversion.

This server bridges a stdio MCP server to HTTP, allowing MCP clients that communicate over HTTP to use the server's tools. It includes a per-tool allow/deny filter for security.

Provides a standardized MCP interface for interacting with HTTP tools and services, enabling unified API access and management.

Enables assistants to retrieve the current time and convert times between time zones through callable MCP tools over streamable HTTP.

Enables MCP clients to connect to a streamable HTTP server with multiple tools, secured by API key authentication, rate limiting, and health checks via an nginx gateway for Docker-based deployment.

A step-by-step guide and complete working example for building and running an MCP server with streamable HTTP transport using Python, mcp, and FastAPI, enabling AI assistants to access tools over HTTP.

An MCP server that provides utility tools for text analysis, URL metadata fetching, and temperature conversion.

Audits MCP tool descriptions for quality and reliability, scoring them 0-100, detecting smells, and providing rewritten versions for better agent accuracy.

Graph-based tool retrieval for LLM agents. Builds a tool graph from OpenAPI/MCP specs and retrieves multi-step workflows via hybrid search (BM25 + graph traversal + embedding), recovering accuracy from 12% to 82% with 79% fewer tokens. Also works as an MCP Proxy to aggregate multiple servers behind 3 meta-tools.

Enables Claude to search a THING API for matching items and return top results with names and ids, handling empty results and API errors cleanly.

Encoding-aware, indentation-smart file tools for AI coding agents. 20+ tools including read/edit with automatic encoding detection, smart indentation conversion, SSH, SFTP, process management, and system utilities.

Enables agents to submit request/response header literals over stdio and receive deterministic, specification-derived verdicts on CORS, RFC 9111 cache freshness, RFC 9110 conditional requests, and Vary matching, complete with rule IDs and clause references. Runs entirely offline with no third-party runtime dependencies, returning byte-identical output for identical input.

An MCP server implementation of Anthropic's Think Tool prompt engineering technique that enables Claude to break down complex problems and enhance its reasoning capabilities by providing a simple tool that echoes back thoughts.

Enables AI agents to execute HTTP/REST/GraphQL requests against any endpoint, including private VPN hosts and local services, while logging untruncated headers, request bodies, and raw responses into a local SQLite audit history. Agents can then search that history by keyword, date, method, or status, retrieve full details by ID, and export everything to JSON.

Enables AI assistants to leverage Qwen's code analysis capabilities with large context windows, supporting file/directory analysis, sandbox execution, and multiple approval modes for safe code operations.

Enables interaction with Jenkins servers through HTTP APIs, allowing users to list jobs, trigger builds, view logs, and manage configurations without requiring plugins or admin access.

Enables AI assistants to interact with Gitea repositories through intelligent tools for issue/PR management, workflow analysis, compliance checking, and content generation, plus 200+ CLI commands for complete CRUD operations.

Enables LLM agents and clients to discover and invoke tools — echo, add, sqrt, and now_utc — over a newline-delimited JSON-RPC 2.0 stdio transport, with protocol handshakes, tool schemas, and in-band tool errors handled per the Model Context Protocol spec. It also includes a demo client for driving the server.

Integrates the Atlassian Rovo Dev CLI with the Model Context Protocol, allowing AI assistants to perform deep code analysis using Rovo Dev's large context capabilities. It enables features like repository-wide queries, file-specific analysis, and specialized coding modes through a standard MCP interface.

This is a powerful Model Context Protocol (MCP) server that integrates multiple AI coding agents—Anthropic Claude Code, OpenAI Codex, and Google Gemini—directly into your workflow. It enables seamless cross-provider analysis, leveraging Gemini's massive token window, Codex's specialized coding capabilities, and Claude's advanced reasoning.

MCP server that builds itself by creating new tools as needed based on user requests (Requires restart of Claude Desktop to use newly created tools).

A Model Context Protocol server that enables AI assistants to interact with Google Gemini CLI, allowing them to leverage Gemini's large token window for analyzing files and codebases using natural language commands.

Enables on-demand generation of TypeScript tools using an LLM with human-in-the-loop approval and safe sandboxed execution. It allows users to create and persist custom functionality through natural language requests.