Integrates with OpenAI's embedding models (text-embedding-ada-002, text-embedding-3-small, text-embedding-3-large) for generating vector embeddings that can be stored and queried in Pinecone for RAG applications and semantic search
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 keyPINECONE_ENVIRONMENT
(required): Your Pinecone environment
How to get credentials:
Go to app.pinecone.io
Sign up or log in
Navigate to API Keys section
Copy your API key
Note your environment (e.g.,
us-west1-gcp
,us-east-1-aws
)Store as
PINECONE_API_KEY
andPINECONE_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:
create_index
Create a new vector index.
Parameters:
name
(string, required): Index namedimension
(int, required): Vector dimensionmetric
(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:
describe_index
Get index configuration and status.
Example:
delete_index
Delete an index.
Example:
Vector Operations
upsert_vectors
Insert or update vectors with metadata.
Parameters:
index_name
(string, required): Index namevectors
(list, required): List of vector objectsnamespace
(string, optional): Namespace (default: "")
Vector format:
Example:
query_vectors
Query similar vectors.
Parameters:
index_name
(string, required): Index namevector
(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:
Response:
fetch_vectors
Fetch vectors by IDs.
Example:
update_vector
Update vector values or metadata.
Example:
delete_vectors
Delete vectors.
Example:
Statistics & Utility
describe_index_stats
Get index statistics.
Example:
list_vector_ids
List all vector IDs.
Example:
create_collection
Create a collection (backup) from an index.
Example:
Namespaces
Namespaces provide data isolation within an index:
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:
RAG Example with OpenAI
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
Match dimensions: Ensure vector dimensions match index
Use namespaces: Separate prod/test/dev data
Add metadata: Enable hybrid search and filtering
Batch upserts: Insert multiple vectors per request
Use serverless: For most applications (cost-effective)
Monitor usage: Track vector count and operations
Create backups: Use collections for important data
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
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.