Pinecone Developer MCP

{ "$schema": "http://json-schema.org/draft-07/schema#", "additionalProperties": false, "properties": { "embed": { "additionalProperties": false, "description": "Configure an embedding model that converts text into a vector.", "properties": { "fieldMap": { "additionalProperties": false, "description": "Identify which field from your data records will be embedded.", "properties": { "text": { "description": "The name of the field in the data records that contains the text\n content to embed. Records in the index must contain this field.", "type": "string" } }, "required": [ "text" ], "type": "object" }, "model": { "description": "Choose an embedding model:\n - \"multilingual-e5-large\" is an efficient dense embedding model\n trained on a mixture of multilingual datasets. It works well on messy\n data and short queries expected to return medium-length passages of\n text (1-2 paragraphs).\n - \"llama-text-embed-v2\" is a high-performance dense embedding model\n optimized for text retrieval and ranking tasks. It is trained on a\n diverse range of text corpora and provides strong performance on\n longer passages and structured documents.\n - \"pinecone-sparse-english-v0\" is a sparse embedding model for\n converting text to sparse vectors for keyword or hybrid search. The\n model directly estimates the lexical importance of tokens by\n leveraging their context.", "enum": [ "multilingual-e5-large", "llama-text-embed-v2", "pinecone-sparse-english-v0" ], "type": "string" } }, "required": [ "model", "fieldMap" ], "type": "object" }, "name": { "description": "A unique name to identify the new index.", "type": "string" } }, "required": [ "name", "embed" ], "type": "object" }