A Model Context Protocol server that provides unified access to multiple LLM APIs including ChatGPT, Claude, and DeepSeek, allowing users to call different LLMs from MCP-compatible clients and combine their responses.
Evaluates RAG outputs on faithfulness, answer relevancy, and context precision using an LLM-as-a-Judge backend. Exposes tools for running evaluations, scoring individual samples, and checking thresholds, enabling CI gating and on-demand assessment via MCP.
Enables creation of persistent, compounding knowledge bases using Karpathy's LLM Wiki pattern with LLM-maintained markdown wikis. Supports automated ingestion, cross-referencing, synthesis, and linting of sources as an alternative to traditional RAG systems.
An MCP server that routes LLM requests across multiple providers and orchestrates other MCP servers, with a focus on local privacy for embeddings and memory.
MCP for Azure DevOps Boards is a MCP server that lets your favourite AI browse, query and update Azure DevOps work items as if it were a project manager. Written in Rust and optimized for tokens usagem, It runs via stdio or HTTP mode and uses standard Azure authentication with az login.
Enables Claude to automatically extract entities and relationships from URLs, PDFs, and YouTube videos to build structured knowledge graphs in Neo4j. It supports custom schemas, academic citation extraction, and community detection for advanced research and content analysis.
A Model Control Protocol server that integrates with Claude Desktop to enable simultaneous querying and cross-checking of responses from multiple LLM providers including OpenAI, Anthropic, Perplexity AI, and Google Gemini.
Enables AI agents to interact with a persistent knowledge graph backend using MCP tools for reading, searching, and analyzing wiki pages with vector search and graph algorithms.
Enables LLMs to store, search, and manage memories with hybrid semantic and keyword search using ChromaDB and Neo4j for persistent memory and knowledge graph capabilities.
An MCP server that provides AI assistants with long-term semantic memory using a Markdown-based knowledge base with hybrid search (vector + FTS5) and syntax-aware chunking. It supports idempotent ingestion, project isolation, and integrity linting.
A Python server implementing the Model Context Protocol to provide customizable prompt templates, resources, and tools that enhance LLM interactions in the continue.dev environment.
Enables chat with multiple LLM providers (OpenAI and Anthropic) while maintaining persistent conversation memory. Provides extensible tool framework for various operations including echo functionality and conversation storage/retrieval.
Turns any static website into an MCP-searchable knowledge base by deploying a Cloudflare Worker that provides full-text search tools, enabling AI assistants to search and retrieve content from your site.
An integration server implementing the Model Context Protocol that enables LLM applications to interact with Milvus vector database functionality, allowing vector search, collection management, and data operations through natural language.