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Model Context Protocol server for Pinecone - enables AI assistants to interact with Pinecone indexes and documentation
Pinecone MCP demonstrates strong naming conventions, comprehensive descriptions, and well-structured schemas across 9 tools. All tools use action verbs (list-, describe-, create-, upsert-, search-, rerank-, cascading-search-, search-). Descriptions are detailed and contextual, ranging 100-400+ characters. Schemas are properly typed with Zod and include enums for constrained inputs. However, three critical issues reduce the score: (1) Array parameter 'records' in upsert-records and 'documents' in rerank-documents lack item-level schema definition, making it impossible for LLMs to understand what structure each element must have; (2) 'query' object parameters in search-records and cascading-search are documented as objects but no nested schema is visible, creating ambiguity about required vs optional fields; (3) The tool parameter descriptions could be more explicit about dependencies (e.g., cascading-search explicitly states reranking is required, but search-records does not clarify when rerank is optional). The server includes excellent discovery guidance (e.g., describe-index-for-model advises calling describe-index before upsert-records) and proper error handling patterns via formatError(). Tool annotations (readOnlyHint/destructiveHint) are present on register-tool.ts, showing awareness of pattern:command-tool. LLM caller metadata (llm_provider, llm_model) is injected server-side for usage tracking, a best practice. Overall, this is a mature, well-maintained server that exceeds most community toolkits in clarity and structure.
Search across multiple indexes for records that are similar to the query text, then deduplicate and rerank the combined results. Reranking is required for this tool. Only works with integrated-inference indexes. To search a single index, use search-records instead.
Create a Pinecone index with integrated inference, which automatically embeds the text field named in "fieldMap". Supports AWS, GCP, and Azure cloud providers. This call blocks until the index is ready to accept upserts and queries, so no readiness polling is needed afterwards. If an index with the same name already exists, it is returned unchanged instead of erroring.
Describe the configuration of a Pinecone index, including its embedding model and the "fieldMap" that names the record field holding the text to embed. Call this before upsert-records to learn the required field name. Check "status.ready" in the response to confirm the index can accept upserts and queries.
Describe the statistics of a Pinecone index, including record counts per namespace. Use this to discover which namespaces exist in an index. Newly upserted records can take a few seconds to be indexed and become visible to search results and index stats; if recent data is missing, wait briefly and retry.
Array item schemas missing for upsert-records and rerank-documents. 'records' and 'documents' parameters are typed as arrays but lack itemSchema definitions, preventing LLMs from understanding required record/document structure.
Nested object parameters ('query' in search-records and cascading-search, 'rerank' in cascading-search) lack visible nested schema definitions. LLMs must infer structure from description prose alone.
Some parameter descriptions are generic ('The search query configuration.') without enumerating or hinting at expected fields or format. LLMs must guess whether query supports filtering, pagination, or other options.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | B | 73 | 2026-07-28+ | v2 |
| 2026-03-09 | F | 38 | - | v1 |
List all Pinecone indexes in the project, including each index's configuration and readiness status. Use this to discover valid index names before calling other tools.
Rerank a list of documents using a Pinecone reranking model. Uses the specified reranking model to score and rank documents by relevance to the query.
Search the Pinecone documentation for information about specific topics, features, or API usage.
Search for records in a Pinecone index namespace using a text query. The query is automatically embedded using the index's configured embedding model.
Upsert records into a Pinecone index namespace, automatically embedding text using the index's configured embedding model. Records are inserted or updated based on their ID.
create-index-for-model 'embed' parameter has a deeply nested structure (embed.model, embed.fieldMap.text) that is verbose and may confuse schema parsing. Consider flattening or renaming.
Tool composition: create-index-for-model, upsert-records, and search-records form a workflow, but dependencies are documented in prose only (e.g., describe-index advises calling before upsert). Consider a higher-level 'initialize_and_load_index' tool for common workflows.