Pinecone Query Skill
Search for records in Pinecone integrated indexes using natural language text queries via the Pinecone MCP server.
What is this skill for?
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.
Prerequisites
Required:
- ✅ Pinecone MCP server must be configured - Check if MCP tools are available
- ✅ PINECONE_API_KEY environment variable must be set - Get a free API key at https://app.pinecone.io/?sessionType=signup
- ✅ Index must be an integrated index - Uses Pinecone embedding models (e.g., multilingual-e5-large, llama-text-embed-v2, pinecone-sparse-english-v0)
When NOT to use this skill
Use the CLI skill instead if:
- ❌ Your index is a standard index (no integrated embedding model)
- ❌ You need to query with custom vector values (not text)
- ❌ You need advanced vector operations (fetch by ID, list vectors, bulk operations)
- ❌ Your index uses third-party embedding models (OpenAI, HuggingFace, Cohere)
MCP Limitation: The Pinecone MCP currently only supports integrated indexes. For all other use cases, use the Pinecone CLI skill.
How it works
Utilize Pinecone MCP's search-records tool to search for records within a specified Pinecone integrated index using a text query.
Workflow
IMPORTANT: Before proceeding, verify the Pinecone MCP tools are available. If MCP tools are not accessible:
- Inform the user that the Pinecone MCP server needs to be configured
- Check if
PINECONE_API_KEYenvironment variable is set - Direct them to the MCP setup documentation or the
pinecone-helpskill
- Parse 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:
- If only the index name is provided, use the describe-index tool to retrieve available namespaces and ask the user to choose. - If only a query is provided, use list-indexes to get available indexes, ask the user to pick one, then use describe-index for namespaces if needed.
- Call the
search-recordstool 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).
Troubleshooting
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 and restart their IDE or agent session:
- Terminal:
export PINECONE_API_KEY="your-key" - IDE without shell inheritance: add
PINECONE_API_KEY=your-keyto a.envfile
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.
- If required arguments are missing, prompt the user to supply them, using Pinecone MCP tools as needed (e.g.,
list-indexes,describe-index). - Guide the user interactively through argument selection until the search can be completed.
- If an invalid value is provided for any argument (e.g., nonexistent index or namespace), surface the error and suggest valid options.
Tools Reference
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.- Ask the user interactively to clarify missing information when needed.