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pinecone:mcp

Documents Pinecone MCP server tools for index management and semantic search.

68|12|Updated Dec 17, 2025
Search Tags:#semantic search#rag#vector database#mcp#index management#reranking#pinecone
One-click install
npx skills add https://github.com/pinecone-io/pinecone-claude-code-plugin --skill pinecone-mcp-pinecone-io
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pinecone:mcp
Source: https://github.com/pinecone-io/pinecone-claude-code-plugin/tree/main/skills/mcp
Command: npx skills add https://github.com/pinecone-io/pinecone-claude-code-plugin --skill pinecone-mcp-pinecone-io

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Agents working with Pinecone need an accurate reference of the MCP server's available tools, their parameters, and their constraints, which this Skill provides in one place. ## Core Features & Use Cases - Tool Reference: Documents all eight Pinecone MCP tools including list-indexes, describe-index, create-index-for-model, upsert-records, search-records, cascading-search, and rerank-documents. - Parameter Guidance: Details required and optional parameters for each tool, including embedding models, reranking models, and metadata filter operators. - Use Case: When an agent needs to create an integrated index with an embedding model and then run a filtered semantic search with reranking, it consults this reference to construct correct tool calls. ## Quick Start Ask the agent to list the available Pinecone MCP tools and show the parameters needed to search records in an index.

Frequently Asked Questions about pinecone:mcp

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

FAQPage Schema
What tools does the Pinecone MCP server provide?▼

The Pinecone MCP server exposes eight tools: list-indexes, describe-index, describe-index-stats, create-index-for-model, upsert-records, search-records, cascading-search, and rerank-documents. These cover index management, record ingestion, and semantic search with reranking.

How do I search a Pinecone index using the MCP server?▼

Use the search-records tool with the index name, namespace, query text, and topK. You can optionally add a MongoDB-style metadata filter and a reranking model such as bge-reranker-v2-m3 to refine results.

Does the Pinecone MCP server work with standard indexes?▼

No, the Pinecone MCP only supports integrated indexes created with a built-in Pinecone embedding model. Standard indexes using external embedding models require the Pinecone CLI instead.

How do I create an index with the Pinecone MCP server?▼

Call create-index-for-model with a name, cloud provider, region, embedding model such as llama-text-embed-v2, and a field map specifying which record field contains the text to embed.

What is cascading search in Pinecone MCP?▼

Cascading-search queries multiple indexes simultaneously, then deduplicates and reranks the combined results into a single ranked list. It requires a reranking model and the fields to rerank on.

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