bui21x/vectordb-mcp-server
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A Model Context Protocol (MCP) server that provides Vector Database integration (Pinecone) for AI agents.
The Vector DB MCP Server is designed to facilitate seamless integration of vector databases with AI agents, leveraging the capabilities of Pinecone for efficient vector storage and retrieval. This server supports semantic search, allowing AI agents to perform advanced queries based on the meaning of the data rather than just keywords. It also includes namespace support, enabling the organization of vectors into distinct categories for better management. Metadata storage is another key feature, allowing additional information to be associated with each vector, enhancing the search and retrieval process. The server is equipped with health monitoring capabilities to ensure optimal performance and reliability. By adhering to the Model Context Protocol, this server ensures compatibility and ease of integration with various AI tools and platforms.
Features
- Vector storage and retrieval
- Semantic search capabilities
- Namespace support
- Metadata storage
- Health monitoring
Usages
local integration stdio
python mcp.run(transport='stdio')
remote integration sse
python mcp.run(transport='sse', host="0.0.0.0", port=8000)
remote integration streamable http
yaml
paths:
/mcp:
post:
x-ms-agentic-protocol: mcp-streamable-1.0development framework fastmcp
python
from mcp.server import FastMCP
app = FastMCP('demo')
@app.tool()
async def query(): ...