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pinecone-mcp

Document Pinecone MCP tools for indexing, upserting, searching, and reranking records.

Updated Aug 27, 2026
Search Tags:#mcp#pinecone#semantic-search#vector-search#rerank#upsert#index-management
One-click install
npx skills add https://github.com/Norfolk-Group/H-Analytics --skill pinecone-mcp-norfolk-group
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pinecone-mcp
Source: https://github.com/Norfolk-Group/H-Analytics/tree/main/.claude/archive/agents-skills-snapshot/pinecone-mcp
Command: npx skills add https://github.com/Norfolk-Group/H-Analytics --skill pinecone-mcp-norfolk-group

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This reference documents the Pinecone MCP server tools and clarifies how to list, inspect, create, upsert, search, cascade, and rerank records so agents and developers can use the MCP API correctly and efficiently.

Core Features & Use Cases

  • List and discover available indexes and their basic metadata to understand your project's vector stores.
  • Describe index configuration and statistics to verify cloud, region, dimension, metric, embedding model, and namespace distributions.
  • Create serverless integrated indexes with automatic embeddings, upsert records with the required embedding field, perform semantic search with optional reranking, run cascading searches across multiple indexes, and rerank arbitrary document sets.
  • Use Case: An agent ingests a document corpus into an integrated index, upserts records using the index's field map, executes semantic searches with an optional reranker, and inspects index stats to monitor ingestion completeness.

Quick Start

List all indexes, describe the configuration for a selected index, and run a semantic search with reranking using the pinecone-mcp tool references.

Frequently Asked Questions about pinecone-mcp

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I perform semantic search and reranking on Pinecone indexes using MCP?▼

To perform semantic search and reranking on Pinecone indexes using MCP, you execute a search query with specified filters and topK, then apply an optional reranker to refine the returned records for higher relevance.

What is a cascading search across multiple Pinecone namespaces?▼

A cascading search in Pinecone is a query operation that searches across multiple indexes, retrieving and aggregating relevant records from different namespaces within a single workflow.

How do I upsert records to a Pinecone serverless index with integrated embeddings?▼

To upsert records to a Pinecone serverless index, you provide data structured according to the index's required embedding field mappings, allowing the integrated index to automatically generate and store the vector embeddings.

Can I use Pinecone MCP tools to inspect index configuration and statistics?▼

Yes, you can use Pinecone MCP tools to describe index configurations and retrieve statistics, verifying cloud, region, dimension, metric, embedding model, and namespace distributions for your vector stores.

Does the Pinecone MCP server support creating indexes without integrated embeddings?▼

No, the Pinecone MCP server is restricted to integrated embedding indexes, meaning you must create serverless indexes with automatic embeddings and upsert records using the specified embedding field mappings.

What required parameters do I need to create a serverless index in Pinecone via MCP?▼

Creating a serverless index in Pinecone via MCP requires specifying parameters for the integrated embedding configuration, allowing the system to automatically process embeddings when you upsert records.

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