| name | query |
| description | Query integrated indexes using text with Pinecone MCP. IMPORTANT - This skill ONLY works with integrated indexes (indexes with built-in Pinecone embedding models like multilingual-e5-large). For standard indexes or advanced vector operations, use the CLI skill instead. Requires PINECONE_API_KEY environment variable and Pinecone MCP server to be configured. |
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.
