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Pinecone MCP Server

by NimbleBrainInc
GitHub
Python
MIT License
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Pinecone MCP Server

MCP server for Pinecone vector database. Store and search embeddings for similarity search, semantic search, and RAG (Retrieval Augmented Generation) applications.

Features

  • Index Management: Create, list, describe, and delete vector indexes

  • Vector Operations: Upsert, query, fetch, update, and delete vectors

  • Similarity Search: Find similar vectors with cosine, euclidean, or dot product metrics

  • Metadata Filtering: Hybrid search with metadata filters

  • Namespaces: Data isolation for multi-tenancy

  • Collections: Create backups from indexes

  • Statistics: Get vector counts and index stats

Setup

Prerequisites

  • Pinecone account

  • API key and environment name

Environment Variables

  • PINECONE_API_KEY (required): Your Pinecone API key

  • PINECONE_ENVIRONMENT (required): Your Pinecone environment

How to get credentials:

  1. Go to app.pinecone.io

  2. Sign up or log in

  3. Navigate to API Keys section

  4. Copy your API key

  5. Note your environment (e.g., us-west1-gcp, us-east-1-aws)

  6. Store as PINECONE_API_KEY and PINECONE_ENVIRONMENT

Index Types

Serverless (Recommended)

  • Pay per usage

  • Auto-scaling

  • No infrastructure management

  • Available regions: AWS (us-east-1, us-west-2), GCP (us-central1, us-west1), Azure (eastus)

Pod-based

  • Fixed capacity

  • Dedicated resources

  • More control over performance

  • Higher cost

Vector Dimensions

Match your embedding model:

  • OpenAI text-embedding-ada-002: 1536 dimensions

  • OpenAI text-embedding-3-small: 1536 dimensions

  • OpenAI text-embedding-3-large: 3072 dimensions

  • sentence-transformers/all-MiniLM-L6-v2: 384 dimensions

  • sentence-transformers/all-mpnet-base-v2: 768 dimensions

Distance Metrics

  • cosine - Cosine similarity (recommended for most use cases)

  • euclidean - Euclidean distance

  • dotproduct - Dot product similarity

Available Tools

Index Management

list_indexes

List all indexes in the project.

Example:

indexes = await list_indexes()

create_index

Create a new vector index.

Parameters:

  • name (string, required): Index name

  • dimension (int, required): Vector dimension

  • metric (string, optional): Distance metric (default: 'cosine')

  • spec_type (string, optional): 'serverless' or 'pod' (default: 'serverless')

  • cloud (string, optional): 'aws', 'gcp', or 'azure' (default: 'aws')

  • region (string, optional): Region (default: 'us-east-1')

Example:

index = await create_index( name="my-index", dimension=1536, # OpenAI embeddings metric="cosine", spec_type="serverless", cloud="aws", region="us-east-1" )

describe_index

Get index configuration and status.

Example:

info = await describe_index(index_name="my-index")

delete_index

Delete an index.

Example:

result = await delete_index(index_name="my-index")

Vector Operations

upsert_vectors

Insert or update vectors with metadata.

Parameters:

  • index_name (string, required): Index name

  • vectors (list, required): List of vector objects

  • namespace (string, optional): Namespace (default: "")

Vector format:

{ "id": "vec1", "values": [0.1, 0.2, 0.3, ...], # Must match index dimension "metadata": {"key": "value"} # Optional }

Example:

result = await upsert_vectors( index_name="my-index", vectors=[ { "id": "doc1", "values": [0.1, 0.2, ...], # 1536 dimensions "metadata": { "title": "Document 1", "category": "tech", "year": 2024 } }, { "id": "doc2", "values": [0.3, 0.4, ...], "metadata": { "title": "Document 2", "category": "science" } } ], namespace="production" )

query_vectors

Query similar vectors.

Parameters:

  • index_name (string, required): Index name

  • vector (list, optional): Query vector (use this OR id)

  • id (string, optional): Vector ID to use as query (use this OR vector)

  • top_k (int, optional): Number of results (default: 10)

  • namespace (string, optional): Namespace (default: "")

  • include_values (bool, optional): Include vectors (default: False)

  • include_metadata (bool, optional): Include metadata (default: True)

  • filter (dict, optional): Metadata filter

Example:

# Query by vector results = await query_vectors( index_name="my-index", vector=[0.1, 0.2, ...], # Your query embedding top_k=5, filter={"category": {"$eq": "tech"}, "year": {"$gte": 2023}}, include_metadata=True ) # Query by existing vector ID results = await query_vectors( index_name="my-index", id="doc1", top_k=5 )

Response:

{ "matches": [ { "id": "doc1", "score": 0.95, "metadata": {"title": "Document 1", "category": "tech"} } ] }

fetch_vectors

Fetch vectors by IDs.

Example:

vectors = await fetch_vectors( index_name="my-index", ids=["doc1", "doc2", "doc3"], namespace="production" )

update_vector

Update vector values or metadata.

Example:

# Update values result = await update_vector( index_name="my-index", id="doc1", values=[0.5, 0.6, ...] ) # Update metadata result = await update_vector( index_name="my-index", id="doc1", set_metadata={"category": "updated", "year": 2025} )

delete_vectors

Delete vectors.

Example:

# Delete by IDs result = await delete_vectors( index_name="my-index", ids=["doc1", "doc2"] ) # Delete by filter result = await delete_vectors( index_name="my-index", filter={"year": {"$lt": 2020}} ) # Delete all in namespace result = await delete_vectors( index_name="my-index", delete_all=True, namespace="test" )

Statistics & Utility

describe_index_stats

Get index statistics.

Example:

stats = await describe_index_stats(index_name="my-index") # Returns: dimension, totalVectorCount, indexFullness, namespaces

list_vector_ids

List all vector IDs.

Example:

ids = await list_vector_ids( index_name="my-index", namespace="production", prefix="doc", limit=100 )

create_collection

Create a collection (backup) from an index.

Example:

collection = await create_collection( name="my-backup", source_index="my-index" )

Namespaces

Namespaces provide data isolation within an index:

# Production data await upsert_vectors(index_name="my-index", vectors=[...], namespace="prod") # Test data await upsert_vectors(index_name="my-index", vectors=[...], namespace="test") # Query only production results = await query_vectors(index_name="my-index", vector=[...], namespace="prod")

Metadata Filtering

Filter vectors during queries using metadata:

Operators:

  • $eq - Equal

  • $ne - Not equal

  • $gt - Greater than

  • $gte - Greater than or equal

  • $lt - Less than

  • $lte - Less than or equal

  • $in - In array

  • $nin - Not in array

Examples:

# Simple filter filter={"category": {"$eq": "tech"}} # Range filter filter={"year": {"$gte": 2020, "$lte": 2024}} # Multiple conditions filter={ "$and": [ {"category": {"$eq": "tech"}}, {"year": {"$gte": 2020}} ] } # OR condition filter={ "$or": [ {"category": {"$eq": "tech"}}, {"category": {"$eq": "science"}} ] } # In array filter={"category": {"$in": ["tech", "science", "engineering"]}}

RAG Example with OpenAI

import openai # 1. Generate embedding response = openai.Embedding.create( input="What is machine learning?", model="text-embedding-ada-002" ) query_embedding = response['data'][0]['embedding'] # 2. Query Pinecone results = await query_vectors( index_name="knowledge-base", vector=query_embedding, top_k=3, include_metadata=True ) # 3. Get context from results context = "\n".join([match['metadata']['text'] for match in results['matches']]) # 4. Generate answer with context answer = openai.ChatCompletion.create( model="gpt-4", messages=[ {"role": "system", "content": f"Answer based on this context:\n{context}"}, {"role": "user", "content": "What is machine learning?"} ] )

Rate Limits

Free Tier (Starter)

  • 100,000 operations/month

  • 1 pod/index

  • 100 indexes max

Paid Tiers

  • Standard: $70/month, unlimited operations

  • Enterprise: Custom pricing, dedicated support

Best Practices

  1. Match dimensions: Ensure vector dimensions match index

  2. Use namespaces: Separate prod/test/dev data

  3. Add metadata: Enable hybrid search and filtering

  4. Batch upserts: Insert multiple vectors per request

  5. Use serverless: For most applications (cost-effective)

  6. Monitor usage: Track vector count and operations

  7. Create backups: Use collections for important data

  8. Optimize queries: Use appropriate top_k values

Common Use Cases

  • Semantic Search: Find similar documents or products

  • RAG: Retrieval for LLM context

  • Recommendation Systems: Similar item recommendations

  • Duplicate Detection: Find near-duplicate content

  • Anomaly Detection: Identify outliers

  • Image Search: Visual similarity search

  • Chatbot Memory: Store conversation context

Error Handling

Common errors:

  • 401 Unauthorized: Invalid API key

  • 404 Not Found: Index or vector not found

  • 400 Bad Request: Invalid dimensions or parameters

  • 429 Too Many Requests: Rate limit exceeded

  • 503 Service Unavailable: Pinecone service issue

API Documentation

Support

This server cannot be installed

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security - not tested
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license - not tested
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quality - not tested

How are these scores calculated?

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

Enables interaction with Pinecone vector databases for storing and searching embeddings. Supports similarity search, metadata filtering, and vector operations for semantic search and RAG applications.

  1. Features
    1. Setup
      1. Prerequisites
      2. Environment Variables
    2. Index Types
      1. Serverless (Recommended)
      2. Pod-based
    3. Vector Dimensions
      1. Distance Metrics
        1. Available Tools
          1. Index Management
          2. Vector Operations
          3. Statistics & Utility
        2. Namespaces
          1. Metadata Filtering
            1. RAG Example with OpenAI
              1. Rate Limits
                1. Free Tier (Starter)
                2. Paid Tiers
              2. Best Practices
                1. Common Use Cases
                  1. Error Handling
                    1. API Documentation
                      1. Support

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