RAG MCP Server — Vector Database Integrations
Build powerful RAG MCP pipelines. Connect Pinecone MCP, ChromaDB MCP, and Weaviate MCP to Claude Desktop for grounded, accurate AI responses from your vector database.
Featured RAG MCP Servers
Most popular vector database and knowledge base integrations
Pinecone MCP Server
Connect to Pinecone vector database for semantic search and RAG applications
Qdrant MCP Server
Official semantic memory layer for storing and retrieving embeddings
ChromaDB MCP Server
Open-source embedding database for local-first AI applications
Weaviate MCP Server
AI-native vector database with built-in modules for ML models
Vector Database MCP Servers
Store and query embeddings for semantic search and retrieval
Pinecone MCP
ChromaDB MCP
Weaviate MCP
Qdrant MCP Server
Milvus MCP
pgvector MCP
Knowledge Base MCP Servers
Connect documentation platforms and note-taking apps to Claude
Notion MCP Server
Obsidian MCP
Confluence MCP
Roam Research MCP
Document Processing MCP Servers
Chunk, embed, and index documents for retrieval pipelines
LlamaIndex MCP
Unstructured MCP
LangChain MCP
Docling MCP Server
Vector Database Comparison
Choose the right vector database for your RAG pipeline
| Feature | Pinecone | ChromaDB | Weaviate | Qdrant |
|---|---|---|---|---|
| Hosting Model | Managed Cloud | Self-hosted / Local | Cloud / Self-hosted | Cloud / Self-hosted |
| Open Source | No | Yes (Apache 2.0) | Yes (BSD 3) | Yes (Apache 2.0) |
| Hybrid Search | Metadata only | Basic | Yes (BM25 + Vector) | Yes |
| Scale | Billions of vectors | Millions | Billions | Billions |
| Best For | Production at scale | Local development | Enterprise RAG | High performance |
| Pricing | Usage-based | Free / Self-host | Free tier + paid | Free tier + paid |
Building RAG Pipelines with MCP
1. Ingest Documents
Use LlamaIndex or Docling MCP servers to process PDFs, markdown, and web pages into chunks. Configure chunk size and overlap for optimal retrieval.
2. Store Embeddings
Connect Pinecone, ChromaDB, or Weaviate MCP servers to store document embeddings. Index metadata for filtering and hybrid search.
3. Query & Generate
Claude queries your vector database via MCP, retrieves relevant context, and generates accurate, grounded responses with source citations.
Frequently Asked Questions
Does Claude use a vector database?
Claude itself doesn't have a built-in vector database, but you can connect vector databases to Claude using RAG MCP servers. Pinecone MCP, ChromaDB MCP, and Weaviate MCP allow Claude to query your embeddings and retrieve relevant context for better responses.
What is the best vector database for RAG?
For production RAG pipelines, Pinecone MCP offers managed scalability. ChromaDB MCP is best for local development. Weaviate MCP excels at hybrid search (keywords + vectors). Qdrant MCP provides high performance. Choose based on your scale and hosting preferences.
How do I connect Pinecone to Claude?
Install Pinecone MCP server, add your Pinecone API key to the configuration, and enable it in Claude Desktop. The RAG MCP server handles authentication and queries automatically, letting Claude search your vector database using natural language.
What embeddings does Claude use?
Claude doesn't generate embeddings directly, but RAG MCP servers can use various embedding models. OpenAI text-embedding-3, Cohere Embed, or open-source models through Hugging Face. Choose embedding models that match your use case and language.
What is the difference between ChromaDB and Pinecone MCP?
ChromaDB MCP runs locally and is open-source — great for development. Pinecone MCP is a managed cloud service that scales to billions of vectors. Use ChromaDB MCP for prototyping, Pinecone MCP for production RAG applications.
How do I build a RAG pipeline with MCP?
Connect a document processing server (LlamaIndex MCP) to chunk your documents, an embedding API to generate vectors, and a vector database MCP (Pinecone, ChromaDB) to store them. Claude queries through the RAG MCP server to retrieve context.
Build a RAG MCP Server
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