"MiniMax MCP (Model Context Protocol) GitHub Repository" matching MCP servers:

An MCP server for integrating AI models with client applications, allowing for structured interactions with APIs using command-line arguments for authentication and configuration.

A server implementation demonstrating how AI models can interact with external tools and services through Model Context Protocol, featuring integrations for calculator functions, GitHub repositories, and Google Maps searches.

Wraps MiniMax AI as an autonomous code executor for Claude Code, offloading coding tasks to save Claude subscription tokens.

This server facilitates the invocation of AI models from providers like Anthropic, OpenAI, and Groq, enabling users to manage and configure large language model interactions seamlessly.

Enables AI models to access GitHub repository contents as context, with features to fetch entire repositories, specific file contents, and repository structures for use in AI interactions.

IMCP - Insecure Model Context Protocol The DVWA for AI Security! Welcome to IMCP – a deliberately vulnerable framework that exposes 16 critical security weaknesses in AI/ML systems. Whether you're a security researcher, developer, or educator, IMCP is your playground for hands-on learning about real

A TypeScript wrapper library for the Model Context Protocol SDK that provides a simplified interface for creating MCP servers with tools, resources, and prompts without needing to work directly with the protocol.

A GitHub-based implementation of the Model Context Protocol that enables LLMs to access and manipulate data in a Universal Object Reference (UOR) framework, providing standardized interfaces for object management with decentralized storage.

Anthropic's github MCP server, but better. Support for more endpoints. Including releases and tags, pull request reviews, statuses, rate limit, gists, projects, packages, and even pull request diffs. Indented to be used with MissionSquad's MCP API for secret management (aka your access token).

Enables schema-validated, sandboxed access to 34 Python-powered tools across Pillow, requests, pypdf, pandas, openpyxl, and python-docx, plus an autonomous ReAct converter agent that can grow the catalog by requirement and self-repair at runtime. It also provides a control console and hardened Docker deployment for secure MCP client integration.

A read-only remote MCP gateway that lets MCP-capable agents discover GitHub REST endpoints and run many read-only GitHub reads (files, issues, comments, pull requests, checks) inside a single sandboxed JavaScript execution, returning only the compact result needed. This cuts model/tool round trips and context use, with enforced read-only access, repository allowlisting, and GitHub App authentication.

All In One Model Context Protocol: qdrant, google suite (gmail, calendar, ...), jira, gitlab, CLI, ...

An educational project that deliberately implements vulnerable MCP servers to demonstrate various security risks like prompt injection, tool poisoning, and code execution for training security researchers and AI safety professionals.

An MCP server that keeps raw tool output out of the context window by sandboxing and compressing results (about 98% reduction), persisting file edits, git operations, tasks and decisions in SQLite with BM25 retrieval so the agent resumes after compaction, and enforcing routing across 17 platforms via MCP and hooks. It also encourages the model to write scripts that compute answers and log only results instead of reading large data into context.

Context Mode is an MCP server that reduces context window waste by sandboxing data-heavy tools, tracking session state in SQLite, and promoting code-based analysis over raw data reads, achieving up to 98% context savings.

An MCP server that preserves LLM context by intercepting large data outputs and returning only concise summaries or relevant sections. It enables efficient sandboxed code execution, file processing, and documentation indexing across multiple programming languages and authenticated CLIs.

An MCP server that enables processing of massive datasets up to 10M+ tokens using a recursive language model pattern for strategic chunking and analysis. It automates sub-queries and result aggregation using free local inference via Ollama or the Claude API to handle context beyond standard prompt limits.

Enables local AI agents to manage GitHub Codespaces workspaces and execute remote tasks by checking out exact commits, running builds, tests, scripts or temporary services, downloading logs and artifacts, and stopping or exporting managed environments.

A Model Context Protocol server that enables LLMs to read, search, and analyze code files with advanced caching and real-time file watching capabilities.

MCP server that lets AI coding tools control and observe a running Node.js process through the chrome devtools protocol (CDP), via a lightweight Debug Adapter Protocol (DAP) bridge.

A drop-in MCP server implementation for Next.js projects using Vercel MCP Adapter, allowing developers to integrate model context protocol functionality with custom tools, prompts, and resources.

Enables the creation and deployment of API-based MCP tools for Cursor Agent using Cloudflare Workers, streamlining development and integration of custom functionalities.

A Model Context Protocol server that enables AI models to access tools through GitHub authentication and provides comprehensive analytics tracking of tool usage, performance, and user behavior.

Provides AI coding assistants with context optimization tools including targeted file analysis, intelligent terminal command execution with LLM-powered output extraction, and web research capabilities. Helps reduce token usage by extracting only relevant information instead of processing entire files and command outputs.

A Model Context Protocol server that provides tools for interacting with both Radicle (peer-to-peer code collaboration) and GitHub through a unified interface, enabling repository management, synchronization, and collaboration features across both platforms.

MCP server to provide Sketch layout information to AI coding agents like Cursor

A lightweight TypeScript-based MCP server that demonstrates how to build custom MCP tools by implementing a simple addition calculator. Serves as a starting point for building MCP-compatible tools.

A template for deploying MCP servers on Vercel with serverless functions. Includes example tools for rolling dice and getting weather data to demonstrate basic functionality and API integration patterns.

This server lets tools-enabled LLMs work directly with SWI‑Prolog. It supports loading Prolog files, adding/removing facts and rules, listing symbols, and running queries with two modes: deterministic pagination and true engine backtracking.

Context-Pods is a comprehensive development framework for creating, testing, and managing Model Context Protocol (MCP) servers. It provides a Meta-MCP Server that can generate other MCP servers through natural language descriptions or by wrapping existing scripts.