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pinecone-query

Query integrated Pinecone indexes with natural language text and optional rerankers.

Updated Aug 23, 2026
Search Tags:#embeddings#mcp#namespace#pinecone#semantic-search#vector-search#rerank
One-click install
npx skills add https://github.com/Norfolk-Group/H-Analytics --skill pinecone-query-norfolk-group
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pinecone-query
Source: https://github.com/Norfolk-Group/H-Analytics/tree/main/.claude/archive/agents-skills-snapshot/pinecone-query
Command: npx skills add https://github.com/Norfolk-Group/H-Analytics --skill pinecone-query-norfolk-group

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill simplifies querying integrated Pinecone indexes that use built-in embedding models by converting natural language queries into MCP searches and returning ranked, metadata-rich records.

Core Features & Use Cases

  • Natural-language search: Accepts text queries and uses Pinecone MCP to embed and search integrated indexes that host Pinecone embedding models.
  • Interactive argument handling: Lists indexes, describes namespaces, and prompts the user when required arguments (query, index, or namespace) are missing.
  • Reranking and formatting: Supports optional reranker models to improve relevance and presents results with IDs, scores, and highlighted metadata for easy review.
  • Use Case: Quickly locate property records or thematic content in a hospitality dataset stored in an integrated Pinecone index.

Quick Start

Search the integrated index hotels-embeddings for boutique hospitality occupancy trends using a natural language query.

Frequently Asked Questions about pinecone-query

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

FAQPage Schema
How do I query a Pinecone index using natural language text?▼

Semantic search on integrated Pinecone indexes uses natural language text queries via the Pinecone MCP server to embed and search records, returning ranked, metadata-rich results. You provide a text query and the target index name.

What do I need to set up before searching integrated Pinecone indexes for semantic search?▼

Searching integrated Pinecone indexes requires a configured Pinecone MCP server and a PINECONE_API_KEY environment variable. The target index must also use Pinecone-hosted embedding models to process the natural language query.

Can I refine semantic search results with a reranker in Pinecone?▼

Yes, you can optionally specify a reranker model when querying an integrated Pinecone index. Applying a reranker refines the semantic search results to improve relevance before returning the final ranked records with IDs, scores, and metadata.

How does namespace filtering work when searching a Pinecone vector index?▼

Namespace filtering targets specific record partitions within a Pinecone index. If you omit the namespace during a query, the Skill describes available namespaces and prompts you to select one interactively to narrow the semantic search scope.

What happens if I do not specify an index name for my vector search?▼

If the index name is missing, the Skill uses the list-indexes and describe-index tools to interactively guide argument selection. It prompts you to choose an available integrated Pinecone index before executing the semantic search.

Does this approach work for searching standard Pinecone indexes without integrated embedding models?▼

No, this semantic search approach applies exclusively to integrated Pinecone indexes that host built-in Pinecone embedding models. It relies on the Pinecone MCP server to embed the text query and search those specific integrated indexes.

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