npx claudepluginhub pinecone-io/pinecone-claude-code-plugin --plugin pineconequery [q] index [indexName] namespace [ns] topK [k] reranker [rerankModel]This skill is limited to using the following tools:
Search for records in Pinecone integrated indexes using natural language text queries via the Pinecone MCP server.
Documents Pinecone MCP tools for integrated indexes: list-indexes, describe-index, create-index-for-model, upsert-records, search-records. Activates for agents needing tool references or parameters.
Provides patterns and Python templates for similarity search with vector databases, including metrics, indexes, and Pinecone implementation. Use for semantic search, RAG, recommendations, and scaling.
Provides setup and TypeScript examples for Upstash Vector DB: semantic search, namespaces, auto-embedding with MixBread. Use for vector search features on Vercel.
Share bugs, ideas, or general feedback.
Search for records in Pinecone integrated indexes using natural language text queries via the Pinecone MCP server.
This skill provides a simple way to query integrated indexes (indexes with built-in Pinecone embedding models) using text queries. The MCP server automatically converts your text into embeddings and searches the index.
Required:
Use the CLI skill instead if:
MCP Limitation: The Pinecone MCP currently only supports integrated indexes. For all other use cases, use the Pinecone CLI skill.
Utilize Pinecone MCP's search-records tool to search for records within a specified Pinecone integrated index using a text query.
IMPORTANT: Before proceeding, verify the Pinecone MCP tools are available. If MCP tools are not accessible:
PINECONE_API_KEY environment variable is setpinecone:help skillParse the user's input for:
query (required): The text to search for.index (required): The name of the Pinecone index to search.namespace (optional): The namespace within the index.reranker (optional): The reranking model to use for improved relevance.If the user omits required arguments:
describe-index tool to retrieve available namespaces and use AskUserQuestion to let the user choose.list-indexes to get available indexes, use AskUserQuestion for the user to pick one, then use describe-index for namespaces if needed.Call the search-records tool with the gathered arguments to perform the search.
Format and display the returned results in a clear, readable table including field highlights (such as ID, score, and relevant metadata).
PINECONE_API_KEY is required. Get a free key at https://app.pinecone.io/?sessionType=signup
If you get an access error, the key is likely missing. Ask the user to set it:
export PINECONE_API_KEY="your-key"
IMPORTANT At the moment, the /query command can only be used with integrated indexes, which use hosted Pinecone embedding models to embed and search for data. If a user attempts to query an index that uses a third party API model such as OpenAI, or HuggingFace embedding models, remind them that this capability is not available yet with the Pinecone MCP server.
list-indexes, describe-index).search-records: Search records in a given index with optional metadata filtering and reranking.list-indexes: List all available Pinecone indexes.describe-index: Get index configuration and namespaces.describe-index-stats: Get stats including record counts and namespaces.rerank-documents: Rerank returned documents using a specified reranking model.