"Guide to Using LangChain Correctly" matching MCP servers:

Provides semantic search across the entire LangChain ecosystem, including documentation and source code for LangChain, LangGraph, LangSmith, and DeepAgents.

An MCP server exposing document search, retrieval, and summarization tools, paired with a LangChain agent that performs multi-step tasks like finding and summarizing relevant docs.

Exposes LangChain and Anthropic Claude capabilities as tools for generating production-ready RAG systems, Supabase vector stores, and document ingestion pipelines. It enables users to instantly scaffold AI infrastructure and document processing code through natural language prompts in MCP-compatible clients.

Exposes LangChain agent capabilities through the Model Context Protocol, enabling multi-step reasoning tasks with ReAct pattern execution via a production-ready FastAPI service deployed on Google Cloud Run.

An MCP server integrating LangChain, RAG, and Agent to provide knowledge retrieval and tool invocation through natural language.

Enables grounding AI responses in a local document corpus by exposing MCP tools to list, search, and summarize documents, and generating answers using OpenAI.

Improves AI agent task performance by providing task area segmentation, hierarchical RAG, and hybrid search capabilities.

Provides semantic search capabilities over the Plesk Extensions Guide documentation using Retrieval-Augmented Generation (RAG) and vector embeddings. It enables AI assistants to retrieve relevant technical information and answer natural language queries regarding Plesk extension development.

Enables MCP clients such as Cline, Cursor, and Claude Desktop to query official LangChain documentation using hybrid retrieval, returning source-linked snippets and full documents to ground answers and reduce hallucination.

A basic Model Context Protocol server implementation that demonstrates core functionality including tools and resources for AI chat applications.

A Multi-Server Control Plane system that enables natural language querying of job listings and employee feedback data through two specialized servers built with LangChain.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

Enables searching and retrieving documentation from crawled documentation sites as an MCP server, allowing coding agents to query real docs instead of relying on training data.

A tool that creates MCP (Model Context Protocol) servers from OpenAPI/Swagger specifications, enabling AI assistants to interact with your APIs.

Enables LLMs to query documents using semantic search, supporting PDFs, Word, Excel, and more. Organizes documents by topics from folder structure and provides advanced search features like phrase matching and date filtering.

A guide for implementing Model Context Protocol (MCP) servers that provide AI models with external tools like web search, text manipulation, and mathematical operations.

A semantic-memory MCP server that stores text 'memories' with provenance and enables recall by meaning (vector search), keyword (FTS5), or structured filters.

A Cloudflare Worker that transforms Cloudflare AI Search (AutoRAG) instances into an MCP server for querying documentation. It enables AI models to search and retrieve relevant information from custom document sets stored in R2 buckets.

Enables querying a hybrid-retrieval RAG pipeline (dense + BM25) over ingested PDF documents, returning answers generated by Gemini.

Enables Claude Desktop and other AI tools to interact directly with Figma, allowing for powerful AI-assisted design capabilities through natural language commands.