Search & RAG Stack for Claude Desktop
Configuration
{
"mcpServers": {
"pinecone-mcp": {
"command": "npx",
"args": [
"-y",
"@pinecone-database/mcp"
],
"env": {
"PINECONE_API_KEY": "YOUR_PINECONE_API_KEY"
}
},
"weaviate-mcp": {
"command": "npx",
"args": [
"-y",
"mcp-server-weaviate"
],
"env": {
"WEAVIATE_URL": "YOUR_WEAVIATE_URL",
"WEAVIATE_API_KEY": "YOUR_WEAVIATE_API_KEY"
}
},
"exa-mcp": {
"command": "npx",
"args": [
"-y",
"exa-mcp-server"
],
"env": {
"EXA_API_KEY": "YOUR_EXA_API_KEY"
}
},
"context7-mcp": {
"command": "npx",
"args": [
"-y",
"@upstash/context7-mcp"
]
},
"firecrawl-mcp": {
"command": "npx",
"args": [
"-y",
"firecrawl-mcp"
],
"env": {
"FIRECRAWL_API_KEY": "YOUR_FIRECRAWL_API_KEY"
}
},
"memory-mcp": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-memory"
]
}
}
}Where to save
Paste the config above into:
~/Library/Application Support/Claude/claude_desktop_config.jsonEnvironment Variables
Replace the YOUR_ placeholders with your actual values.
PINECONE_API_KEYrequiredPinecone API key for authentication
Used by: Pinecone MCP
WEAVIATE_URLrequiredWeaviate instance URL
Used by: Weaviate MCP
WEAVIATE_API_KEYWeaviate API key for authentication
Used by: Weaviate MCP
EXA_API_KEYrequiredExa API key
Used by: Exa Search MCP
FIRECRAWL_API_KEYrequiredFirecrawl API key
Used by: Firecrawl MCP
What’s in this stack
Search Pinecone documentation, manage vector indexes, upsert data, query for relevant information, and rerank results from your AI editor.
Store and query high-dimensional vector embeddings at scale for fast, accurate semantic search across your knowledge base.
Connect to Weaviate vector database collections for semantic search, knowledge base operations, and chat memory storage from your AI editor.
Run hybrid vector and keyword search with built-in vectorization modules — great for multi-modal RAG pipelines.
AI-powered web search and crawling with Exa. Get semantically relevant results, extract content, and find similar pages.
Perform AI-native web search that understands semantic meaning, retrieving the most relevant sources for your RAG context window.
Pull up-to-date documentation and code examples for any library directly into your prompt context using the Context7 API.
Pull current library and framework documentation into your retrieval pipeline so generated answers reflect the latest APIs.
Scrape and crawl websites, extract structured data, and perform batch web scraping with LLM-powered content analysis.
Crawl and extract clean content from any website to build and refresh your RAG knowledge base automatically.
Enable persistent memory for Claude through a knowledge graph. Store and retrieve entities, relations, and observations across sessions.
Persist conversation context, retrieved facts, and user preferences across sessions for continuity in multi-turn RAG applications.