"Connecting MCP Servers to a Local Project" matching MCP servers:

A workspace of six MCP servers—grounded-cv (hybrid RAG with citations), repo-intel (GitHub repo intelligence), web-research (safe web search/extraction), ats-jobs (unified ATS job boards), outcome-ledger (durable task ledger), and coding-workflows (code review & planning)—purpose-built for agentic hiring evaluations.

Open-source MCP servers for ESG data extraction, analysis, and regulation management, providing 31 tools across 6 servers for tasks like metrics extraction, PDF processing, vector storage, and web scraping.

A Multi-Agent Conversation Protocol Server that provides access to Google's Local Services API, enabling interaction with local service businesses information through natural language commands.

Local workspace memory for Claude Desktop. Indexes documents into a vector store with hybrid search, cross-session memory, auto-learn, and knowledge graph.

A self-contained Claude Code plugin that profiles a project by running a fixed DAG to emit agent-facing context pages, a human/developer guide, and a queryable vectorstore.

It enables users to interact with a codebase through hybrid retrieval, code graph navigation, impact analysis, and context preparation for AI coding agents.

Routes coding agents to the most relevant project documentation (decisions, intent, constraints) with provenance and freshness, providing tools for task routing, knowledge search, and document context.

Enables coding agents to retrieve compact, cited repository maps through MCP tools such as search, symbol inspection, flow investigation, and deep context loading.

Enables AI tools to search and manage a private local knowledge base via MCP or HTTP, using local Chinese semantic retrieval without sending data externally.

Stores manual project memories, enables semantic retrieval, and tracks per-memory confidence using PostgreSQL with pgvector and OpenAI-compatible embeddings.

Enables users to interact with an e-commerce shopping assistant using natural language to search products, check prices and ratings, compare items, and get personalized recommendations.

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.

A local-first document retrieval MCP server that enables AI coding tools like Codex to search private local documents via semantic search and keyword boost, supporting ingestion of PDF, DOCX, TXT, Markdown, and HTML files.

MCP server that enables local hybrid semantic and keyword search over private PDF, DOCX, Markdown, and text documents without sending data to embedding APIs.

Privacy-first local document search using semantic search. Runs entirely on your machine with no cloud services, supporting PDF, DOCX, TXT, and Markdown files.

Enables developers to index and search their own local PDF, DOCX, Markdown, and text documents from an MCP client or terminal, combining semantic similarity with keyword matching for exact technical terms. Everything runs on the user's machine with no API key, Docker, or external database, so private documents stay private and work offline after the initial model download.

A fully local RAG MCP server for semantic code search and code intelligence, using AST-level chunking and hybrid search to pinpoint functions, classes, and APIs. No cloud, no API keys, zero setup.

A Python MCP server that gives LLMs persistent, searchable access to project context — documentation, architecture decisions, and session notes.

Semantic search over project-specific decision rules using Chroma, enabling LLMs to retrieve relevant context.

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.