{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pinecone-mcp-server","slug":"pinecone-mcp-server","name":"Pinecone MCP Server","type":"mcp","url":"https://github.com/pinecone-io/pinecone-mcp","page_url":"https://unfragile.ai/pinecone-mcp-server","categories":["mcp-servers","rag-knowledge"],"tags":["pinecone","vector-database","embeddings","official"],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pinecone-mcp-server__cap_0","uri":"capability://tool.use.integration.vector.upsert.with.metadata","name":"vector-upsert-with-metadata","description":"Inserts or updates vectors in a Pinecone index with associated metadata and IDs through MCP tool interface. Implements batch upsert operations that accept vector embeddings (float arrays), unique identifiers, and arbitrary JSON metadata, routing them to the Pinecone API with automatic connection pooling and error handling. Supports sparse-dense vector formats for hybrid search scenarios.","intents":["I need to store document embeddings with their source metadata in a vector database for later retrieval","I want to update existing vectors in bulk without re-indexing the entire database","I need to associate custom metadata (timestamps, source URLs, tags) with each vector for filtering"],"best_for":["RAG pipeline builders storing embeddings from LLM-generated or third-party embedding models","Teams building knowledge bases that require metadata-rich vector storage","Developers integrating Pinecone into multi-step AI workflows via MCP"],"limitations":["Batch size limits enforced by Pinecone API (typically 100-1000 vectors per request depending on vector dimension)","Metadata filtering only works on indexed metadata fields — arbitrary JSON filtering not supported","No built-in deduplication — duplicate IDs will overwrite previous vectors silently","Upsert latency increases with vector dimension and metadata payload size"],"requires":["Pinecone API key with write permissions","Active Pinecone index with defined dimension matching embedding model output","MCP client compatible with tool-use protocol (Claude, custom agents)","Pre-computed embeddings (vectors must be generated externally)"],"input_types":["JSON array of objects with 'id', 'values' (float array), 'metadata' (JSON object)","namespace string (optional, defaults to default namespace)"],"output_types":["JSON response with upsert count and operation status","Error details if batch validation fails"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pinecone-mcp-server__cap_1","uri":"capability://search.retrieval.semantic.similarity.search.with.filters","name":"semantic-similarity-search-with-filters","description":"Queries a Pinecone index using vector similarity search with optional metadata filtering and result ranking. Accepts a query vector (or raw text that gets embedded), performs approximate nearest neighbor search using Pinecone's indexing structure (HNSW or IVF), and returns top-k results with similarity scores. Supports metadata filter expressions to constrain results to specific subsets (e.g., documents from a date range or category).","intents":["I want to find the most relevant documents or chunks from my knowledge base given a user query","I need to retrieve vectors similar to a given embedding while filtering by document source or timestamp","I want to implement semantic search with faceted filtering (e.g., only results from specific categories)"],"best_for":["RAG systems retrieving context for LLM prompts based on semantic relevance","Recommendation engines finding similar items in a product or content catalog","Multi-tenant applications needing per-tenant result isolation via namespace filtering"],"limitations":["Query vectors must match the dimension of the index — dimension mismatch causes API errors","Metadata filtering only works on fields explicitly marked as filterable during index creation","Top-k results are approximate (not exact nearest neighbors) due to HNSW/IVF approximation algorithms","Similarity scores are relative to the index structure and not directly comparable across different indexes","No support for complex boolean filter expressions — only simple equality/range filters"],"requires":["Pinecone API key with read permissions","Active Pinecone index with pre-populated vectors","Query vector with dimension matching index dimension, OR text input with embedding model available","Metadata filter schema defined during index creation if filtering is needed"],"input_types":["Query vector (float array) OR query text string","top_k integer (number of results to return)","filter object (optional, JSON metadata filter expression)","namespace string (optional)"],"output_types":["JSON array of matches with 'id', 'score' (similarity), 'metadata' object","Empty array if no results match filters"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pinecone-mcp-server__cap_2","uri":"capability://tool.use.integration.index.and.namespace.management","name":"index-and-namespace-management","description":"Creates, deletes, and lists Pinecone indexes and namespaces through MCP tools. Manages index configuration (dimension, metric type, pod type) and namespace isolation for multi-tenant or multi-project scenarios. Provides introspection into index statistics (vector count, dimension, metric) and namespace-level operations without direct API calls.","intents":["I need to create a new vector index with specific configuration for a new project or use case","I want to isolate vectors for different tenants or data sources using namespaces","I need to check index statistics and health before running queries"],"best_for":["Multi-tenant SaaS platforms managing separate vector spaces per customer","Teams with multiple AI projects needing isolated vector indexes","DevOps engineers automating Pinecone infrastructure provisioning"],"limitations":["Index creation is asynchronous — tool returns immediately but index may take minutes to become ready","Deletion is permanent and irreversible — no soft-delete or recovery mechanism","Namespace operations are limited to creation/deletion — no namespace-level statistics or metadata","Index configuration (metric type, pod type) cannot be changed after creation — requires recreation","No support for index scaling or pod type changes through MCP interface"],"requires":["Pinecone API key with admin permissions (create/delete index)","Pinecone account with available quota for new indexes","Vector dimension specification before index creation"],"input_types":["Index name (string), dimension (integer), metric type (cosine/euclidean/dotproduct), pod type (s1/p1/p2)","Namespace name (string) for namespace operations"],"output_types":["JSON confirmation with index/namespace details","Index statistics (vector count, dimension, metric, status)","List of existing indexes or namespaces"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pinecone-mcp-server__cap_3","uri":"capability://tool.use.integration.metadata.filtered.vector.deletion","name":"metadata-filtered-vector-deletion","description":"Deletes vectors from a Pinecone index using metadata filter expressions or by explicit ID. Supports bulk deletion by filter (e.g., delete all vectors with timestamp < X) or individual deletion by vector ID. Operates at namespace level and returns count of deleted vectors.","intents":["I need to remove outdated or irrelevant vectors from my index based on metadata criteria","I want to delete specific vectors by ID when they're no longer relevant","I need to clean up vectors for a specific data source or time period"],"best_for":["Knowledge base maintenance workflows that need to purge stale data","Multi-tenant systems removing customer data on account deletion","RAG systems updating indexes with fresh data by deleting old versions"],"limitations":["Deletion by filter is asynchronous and may take time to complete for large datasets","No dry-run mode — deletions are immediate and cannot be rolled back","Complex filter expressions not supported — only simple equality/range filters","Deletion count returned may be approximate for very large filter operations","No transaction support — partial failures in bulk deletes may leave inconsistent state"],"requires":["Pinecone API key with write permissions","Active Pinecone index","Either vector IDs to delete OR metadata filter criteria"],"input_types":["Vector ID (string) for single deletion OR filter object (JSON) for bulk deletion","namespace string (optional)"],"output_types":["JSON confirmation with number of vectors deleted","Error details if filter is invalid"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pinecone-mcp-server__cap_4","uri":"capability://memory.knowledge.namespace.scoped.vector.operations","name":"namespace-scoped-vector-operations","description":"Isolates all vector operations (upsert, query, delete) to specific namespaces within a Pinecone index. Namespaces provide logical partitioning of vectors without requiring separate indexes, enabling multi-tenant or multi-project scenarios. Each operation accepts an optional namespace parameter that routes to the correct partition.","intents":["I need to store vectors for multiple customers in one index while keeping them logically separated","I want to query only vectors from a specific project or data source without affecting others","I need to delete or update vectors for one tenant without impacting others"],"best_for":["SaaS platforms with multiple customers sharing infrastructure","Multi-project organizations needing logical data isolation without index overhead","Teams managing different data sources or time periods in a single index"],"limitations":["Namespaces are logical only — no encryption or strong isolation guarantees","No namespace-level access control — API key grants access to all namespaces in an index","Namespace names must be unique within an index — no hierarchical namespacing","No namespace-level statistics or monitoring — only index-level metrics available","Deleting a namespace requires deleting all vectors in it (no explicit namespace deletion)"],"requires":["Pinecone API key","Active Pinecone index","Namespace name (string) for scoped operations"],"input_types":["namespace parameter (string) passed to upsert, query, or delete operations"],"output_types":["Same as underlying operation (upsert/query/delete), scoped to specified namespace"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pinecone-mcp-server__cap_5","uri":"capability://search.retrieval.sparse.dense.hybrid.vector.search","name":"sparse-dense-hybrid-vector-search","description":"Supports hybrid search combining sparse vectors (keyword/BM25 style) and dense vectors (semantic embeddings) in a single query. Accepts both sparse and dense vector representations, performs weighted combination of results, and returns unified ranked results. Enables keyword-aware semantic search without separate keyword index.","intents":["I want to combine keyword matching with semantic similarity for more relevant search results","I need to search documents using both exact term matching and semantic understanding","I want to weight keyword matches and semantic matches differently based on use case"],"best_for":["Enterprise search systems needing both precision (keywords) and recall (semantics)","Legal/compliance document search requiring exact term matching plus relevance ranking","E-commerce search combining product attributes with semantic understanding"],"limitations":["Sparse vectors must be pre-computed (typically from BM25 or TF-IDF) — no built-in tokenization","Hybrid search requires index configured for sparse-dense support — not available on all index types","Weighting between sparse and dense results is fixed at query time — no per-result weighting","Sparse vector format is Pinecone-specific — not compatible with other vector databases","Performance overhead compared to dense-only search due to dual index traversal"],"requires":["Pinecone index configured with sparse-dense support","Both sparse vector (indices and values) and dense vector (float array) for each query","Pre-computed sparse vectors from external BM25/TF-IDF engine"],"input_types":["sparse_vector object with 'indices' (integer array) and 'values' (float array)","dense_vector (float array)","top_k integer","filter object (optional)"],"output_types":["JSON array of matches with 'id', 'score' (combined sparse+dense), 'metadata'"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pinecone-mcp-server__cap_6","uri":"capability://search.retrieval.batch.vector.query.with.result.aggregation","name":"batch-vector-query-with-result-aggregation","description":"Executes multiple vector queries in a single MCP call and aggregates results with optional deduplication and ranking. Accepts array of query vectors or text queries, performs parallel similarity search for each, and returns combined ranked results. Useful for multi-query retrieval patterns (e.g., query expansion, multi-hop reasoning).","intents":["I want to search for multiple related concepts simultaneously and get combined results","I need to perform query expansion (generate multiple queries and merge results) in one operation","I want to retrieve context for multi-hop reasoning by querying for intermediate concepts"],"best_for":["Advanced RAG systems using query expansion or multi-query retrieval","Agents performing multi-step reasoning that requires context from multiple angles","Search systems needing to handle ambiguous queries by exploring multiple interpretations"],"limitations":["Batch query latency is sum of individual query latencies (no true parallelization guarantee)","Result deduplication is ID-based only — similar but non-identical results not merged","Ranking across multiple queries is not standardized — score normalization required","No built-in query expansion — expansion logic must be implemented in agent","Batch size limits may apply depending on Pinecone plan"],"requires":["Pinecone API key with read permissions","Active Pinecone index","Array of query vectors or text queries"],"input_types":["Array of query objects, each with 'vector' (float array) or 'text' (string), 'top_k' (integer), optional 'filter'"],"output_types":["JSON array of aggregated results with 'id', 'score', 'metadata', 'query_index' (which query matched)"],"categories":["search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pinecone-mcp-server__cap_7","uri":"capability://tool.use.integration.vector.dimension.and.metric.introspection","name":"vector-dimension-and-metric-introspection","description":"Retrieves index metadata including vector dimension, similarity metric (cosine/euclidean/dotproduct), vector count, and index status. Provides runtime introspection for agents to validate query vectors and understand index configuration without external documentation.","intents":["I need to verify that my query vectors match the index dimension before querying","I want to check if an index is ready for queries or still initializing","I need to understand which similarity metric an index uses to interpret scores correctly"],"best_for":["Agents validating inputs before performing operations","Debugging tools checking index configuration","Multi-index systems routing queries to appropriate indexes based on dimension"],"limitations":["Metadata is read-only — no ability to change metric or dimension after index creation","Vector count is approximate for very large indexes","No per-namespace statistics — only index-level metrics","Status information is limited to basic ready/initializing states"],"requires":["Pinecone API key with read permissions","Index name"],"input_types":["index_name (string)"],"output_types":["JSON object with 'dimension', 'metric', 'vector_count', 'status', 'pod_type'"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pinecone-mcp-server__cap_8","uri":"capability://safety.moderation.error.handling.and.retry.logic","name":"error-handling-and-retry-logic","description":"Implements automatic retry logic for transient Pinecone API failures (rate limits, temporary outages) with exponential backoff, and provides detailed error messages for permanent failures (invalid API key, dimension mismatch). Handles MCP-specific error responses that include error codes, messages, and recovery suggestions. Reduces noise from transient failures while surfacing actionable errors.","intents":["I want my agent to automatically retry failed vector operations without manual intervention","I need clear error messages that explain why an operation failed and how to fix it","I want to distinguish between transient failures (retry) and permanent errors (fail fast)"],"best_for":["Production LLM agents requiring resilience to Pinecone API hiccups","Teams building autonomous systems that need graceful degradation","Developers debugging vector operation failures with clear error context"],"limitations":["Retry logic uses fixed exponential backoff; no adaptive backoff based on server load","Max retry attempts are hardcoded; no configuration per operation type","Retries only apply to idempotent operations (query, describe); upsert retries may cause duplicates","No circuit breaker pattern; continuous retries may overwhelm a degraded Pinecone service"],"requires":["Pinecone API key with appropriate permissions","MCP client supporting error responses","Network connectivity to Pinecone API"],"input_types":["any Pinecone operation (upsert, query, delete, etc.)"],"output_types":["successful operation result","detailed error response with code, message, and recovery suggestion if all retries exhausted"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pinecone-mcp-server__cap_9","uri":"capability://safety.moderation.authentication.and.credential.management","name":"authentication-and-credential-management","description":"Manages Pinecone API authentication through environment variables or configuration files, supporting secure credential storage without embedding keys in code. Implements credential validation at startup and automatic re-authentication if tokens expire. Supports both API key and OAuth-based authentication (if Pinecone supports it), with fallback mechanisms for missing credentials.","intents":["I want to securely pass my Pinecone API key to the MCP server without hardcoding it","I need the MCP server to validate credentials at startup and fail fast if auth is invalid","I want to rotate API keys without restarting the MCP server"],"best_for":["DevOps engineers deploying Pinecone MCP servers in production environments","Teams managing multiple Pinecone projects with different API keys","Security-conscious organizations requiring credential isolation"],"limitations":["Credentials are typically passed via environment variables; no built-in secrets manager integration","No automatic key rotation; requires manual credential updates and server restart","API key validation happens at startup; invalid keys detected only when server starts","No audit logging of credential usage; relies on Pinecone API logs"],"requires":["Pinecone API key (from Pinecone console)","Environment variable PINECONE_API_KEY or configuration file with credentials","MCP server process with access to environment or config file"],"input_types":["environment variable or config file path"],"output_types":["authentication success/failure at startup","error message if credentials are invalid or missing"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pinecone-mcp-server__headline","uri":"capability://data.processing.analysis.mcp.server.for.vector.database.operations","name":"mcp server for vector database operations","description":"The Pinecone MCP Server is an official tool designed for managing vector databases, enabling users to efficiently upsert vectors, perform similarity queries, and manage indexes and namespaces.","intents":["best MCP server for vector databases","MCP server for similarity search","vector database management tools","how to manage embeddings with MCP server","best practices for using Pinecone MCP"],"best_for":["developers working with vector data","teams needing efficient similarity searches"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":61,"verified":false,"data_access_risk":"high","permissions":["Pinecone API key with write permissions","Active Pinecone index with defined dimension matching embedding model output","MCP client compatible with tool-use protocol (Claude, custom agents)","Pre-computed embeddings (vectors must be generated externally)","Pinecone API key with read permissions","Active Pinecone index with pre-populated vectors","Query vector with dimension matching index dimension, OR text input with embedding model available","Metadata filter schema defined during index creation if filtering is needed","Pinecone API key with admin permissions (create/delete index)","Pinecone account with available quota for new indexes"],"failure_modes":["Batch size limits enforced by Pinecone API (typically 100-1000 vectors per request depending on vector dimension)","Metadata filtering only works on indexed metadata fields — arbitrary JSON filtering not supported","No built-in deduplication — duplicate IDs will overwrite previous vectors silently","Upsert latency increases with vector dimension and metadata payload size","Query vectors must match the dimension of the index — dimension mismatch causes API errors","Metadata filtering only works on fields explicitly marked as filterable during index creation","Top-k results are approximate (not exact nearest neighbors) due to HNSW/IVF approximation algorithms","Similarity scores are relative to the index structure and not directly comparable across different indexes","No support for complex boolean filter expressions — only simple equality/range filters","Index creation is asynchronous — tool returns immediately but index may take minutes to become ready","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.62,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:05.295Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pinecone-mcp-server","compare_url":"https://unfragile.ai/compare?artifact=pinecone-mcp-server"}},"signature":"dupaFdHGStw2OprZV3VXSu+ae3WDHFb0BEhmgyPPv2itznilTEZkNFAeg4qlKLpztdlV1rcHxPVjyJQ/lJT2AA==","signedAt":"2026-06-22T02:35:59.938Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pinecone-mcp-server","artifact":"https://unfragile.ai/pinecone-mcp-server","verify":"https://unfragile.ai/api/v1/verify?slug=pinecone-mcp-server","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}