A unified Model Context Protocol server implementation that aggregates multiple MCP servers into one.

Agentic intelligence framework for Claude Code and Antigravity: slash commands, subagents, skills, hooks, governance, memory, and cost-aware model routing for AI-driven development.

An official Qdrant Model Context Protocol (MCP) server implementation

Enables searching sticker collections, viewing sticker images for visual models, and sending stickers—optionally with a short caption—to an authorized QQ session through a running QQ bridge.

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.

Keeps large tool outputs out of an agent's context window by spilling them to disk and handing back a short pointer instead. Lets the agent pull only what it needs — exact numbered line ranges read directly from disk, or verbatim relevant lines returned by a small model answering a question about the stored output, plus tools to open, list, and close documents.

Exposes a single ask_warriorgpt tool that lets Claude Code delegate self-contained code generation prompts to Wayne State's hosted model API, passing files to and from disk so the content never counts against the Claude plan. It validates multi-file edits before writing and logs per-session token usage, keeping design and debugging work with Claude.

Mounts a headless, low-memory browser for agents with a text-first tool interface, enabling navigation, semantic tree/markdown extraction, structured-data scraping, screenshots, form interaction, and session capture. Reads and interactions are returned as semantic trees, markdown, or CSS-selector JSON rather than pixels, which is cheaper for a model to reason over.

Context compression for AI coding agents: compresses tool output before it enters the model, dedups repeats to 13-token refs. Claude Code, Cursor, Codex, Kiro, Zed, any MCP client. Rust, zero LLM calls.

Neo4j Labs Model Context Protocol servers

Official MiniMax Model Context Protocol (MCP) server that enables interaction with powerful Text to Speech, image generation and video generation APIs.

Analytics tool for MCP (Model Context Protocol) servers and AI agents - tracks tool usage patterns and provides insights

An MCP (Model Context Protocol) server implementation for Microsoft Teams integration, providing capabilities to read messages, create messages, reply to messages, mention members.

Active Context Pruning (ACP) for the DeepSeek Harness — model-driven context management as a CompactionEngine backend.

A curated list of Model Context Protocol (MCP) servers

See what a conversation feeds the model, what your rules keep out, and what one click would change.

This is a Model Context Protocol (MCP) server that provides comprehensive financial data from Yahoo Finance. It allows you to retrieve detailed information about stocks, including historical prices, company information, financial statements, options data, and market news.

A Model Context Protocol (MCP) that allows Claude Desktop and other AI tools (Claude Code, Cursor, Antigravity, etc.) to read, analyze, and modify Figma designs

Native Model Context Protocol (MCP) server turning AI coding agents into .NET runtime diagnosticians, memory forensic analysts, and CIL binary architects.

A local Python + SQLite MCP memory service that stores persona, shared experiences, facts, agreements, and unfinished work, then restores them within a context budget so a new model can continue the same conversation. It supports offline original-text persistence, Chinese keyword retrieval, provenance-tracked memories, persona versioning/rollback, and progressive fading of older events.

Provides Model Context Protocol access to the SEAF ArchTool REST API, enabling agents to inspect sections and entities, navigate relations, query datasets, and run JSONata expressions against a SEAF portal.

Enables building zero-dependency Model Context Protocol servers over the Streamable HTTP transport, exposing typed tools, resources and URI templates, prompts, background tasks, and OAuth-protected endpoints. It can be mounted into FastAPI, Starlette, Flask, Django or any ASGI/WSGI app, or run standalone on the Python standard library alone.

Enables AI agents to list and compare LLM API prices per million tokens, rank the cheapest models by context and keyword, look up GPU rental rates per hour by provider, and estimate self-hosting cost per million tokens against API pricing. Pricing data comes from OpenRouter's public models endpoint plus a dated GPU price CSV, with source URLs and explicit notes when data is missing.

Enables MCP clients to discover NVIDIA, AMD, and Intel GPUs, monitor utilization, memory, temperature, power, clocks, and processes, retain bounded local history, and estimate whether AI models fit available VRAM before loading them.