query

# 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:** 1. ✅ **Pinecone MCP server must be configured** - Check if MCP tools are available 2. ✅ **PINECONE_API_KEY environment variable must be set** - Get a free API key at https://app.pinecone.io/?sessionType=signup 3. ✅ **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_KEY` environment variable is set - Direct them to the MCP setup documentation or the `help` skill 1. 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. 2. 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. 3. Call the `search-records` tool with the gathered arguments to perform the search. 4. 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-key` to a `.env` file **IMPORTANT** At the moment, the query action 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. ---