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Pinecone MCP

Database•⭐ 1,000

Search and manage Pinecone vector database for production RAG systems and semantic search.

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Overview

Search and manage Pinecone vector database for production RAG systems and semantic search.

Setup

Run with npx:

npx -y @modelcontextprotocol/server-pinecone

Configuration

PINECONE_API_KEY, PINECONE_ENVIRONMENT environment variables

Documentation

Pinecone MCP (Enhanced)

Overview

Pinecone MCP provides access to Pinecone vector database for production RAG systems.

Features

  • Vector indexing
  • Semantic search
  • Metadata filtering
  • Hybrid search
  • Auto-scaling

Installation

npx -y @modelcontextprotocol/server-pinecone

Configuration

Set up Pinecone credentials:

export PINECONE_API_KEY=your_api_key
export PINECONE_ENVIRONMENT=your_environment

Get your credentials from Pinecone Console.

Available Operations

OperationDescription
QueryVector similarity search
UpsertAdd vectors
DeleteRemove vectors
List IndexesGet all indexes
Describe IndexGet index details

Usage Examples

Query

{
  "action": "query",
  "index": "my-index",
  "vector": [0.1, 0.2, 0.3, ...],
  "top_k": 10,
  "filter": { "category": "tech" }
}

Upsert

{
  "action": "upsert",
  "index": "my-index",
  "vectors": [
    {
      "id": "doc1",
      "values": [0.1, 0.2, 0.3, ...],
      "metadata": { "title": "AI Guide", "year": 2026 }
    }
  ]
}

Create Index

{
  "action": "create_index",
  "name": "my-index",
  "dimension": 1536,
  "metric": "cosine",
  "spec": { "serverless": { "cloud": "aws", "region": "us-east-1" } }
}

Pros

  • ✅ Managed service
  • ✅ Auto-scaling
  • ✅ High availability
  • ✅ Simple API

Cons

  • ❌ Requires Pinecone account
  • ❌ Cost at scale
  • ❌ Limited self-hosting

Resources