{"_id":"69b2da6867df398baec12eb5","name":"Pinecone","slug":"pinecone","url":"https://pinecone.io","description":"","logo":"","category":"AI & ML","tags":[],"pricingModel":"unknown","scores":{"tokenEfficiency":{"score":7,"confidence":"scanner","evidence":"Vector database APIs typically return compact numerical embeddings and support pagination/batching, though response efficiency depends on query complexity and result set size which isn't fully documented in the signals.","na":false},"access":{"score":8,"confidence":"scanner","evidence":"Pinecone offers comprehensive programmatic access via REST API, official SDKs for Node.js and Python, LangChain integration, and an MCP server (@pinecone-database/mcp v0.2.1), providing multiple pathways for agent integration.","na":false},"auth":{"score":8,"confidence":"scanner","evidence":"API key-based authentication is standard for vector databases and allows autonomous agent access without human-in-the-loop; no evidence of overly restrictive scoping limitations, though specific permission granularity isn't detailed.","na":false},"speed":{"score":7,"confidence":"scanner","evidence":"Vector search operations are optimized for low-latency retrieval, and Pinecone's infrastructure suggests reasonable performance, but specific rate limits, SLA data, and conditional request support (ETags) are not documented in the available signals.","na":false},"discoverability":{"score":6,"confidence":"scanner","evidence":"While Pinecone has developer documentation and an llms.txt file (43KB), there is no OpenAPI spec found, which limits automated discovery and client generation; documentation quality appears adequate but spec-driven discoverability is absent.","na":false},"reliability":{"score":7,"confidence":"scanner","evidence":"As a production database service, Pinecone likely has versioning and consistent schemas, but signals lack explicit evidence of idempotency keys, API versioning strategy, or a public status page.","na":false},"safety":{"score":6,"confidence":"scanner","evidence":"Vector databases typically support test/sandbox indexes and role-based scoping, but no evidence of dry-run modes, explicit undo operations, or detailed safety mechanisms is present in the collected signals.","na":false},"reactivity":{"score":3,"confidence":"scanner","evidence":"No webhooks, streaming, SSE, or real-time push mechanisms are evident in the signals; Pinecone appears to be a query-response API requiring polling for reactive patterns.","na":false}},"agentGrade":"B","agentScore":6.98,"accessMethods":{"restApi":true,"graphql":false,"cli":false,"sdk":["Node (@pinecone-database/pinecone)","Python (pinecone)"],"mcpServer":"none","openApiSpec":"","llmsTxt":true,"agentsJson":false},"authInfo":{"methods":["unknown"],"scopedPermissions":false,"humanRequired":true},"reviewCount":0,"avgReviewScore":0,"viewCount":149,"badgeEmbedCount":10,"agentSkillSlugs":[],"alternatives":[],"claimed":false,"status":"graded","createdAt":"2026-03-12T15:23:20.709Z","updatedAt":"2026-08-23T22:35:30.360Z","__v":0,"scannerData":{"lastScannedAt":"2026-03-12T19:36:25.296Z","scanVersion":1,"rawSignals":{"homepage":{"status":200,"contentLength":366160,"hasStructuredData":false,"hasDeveloperDocs":true,"hasAgentMentions":true,"responseTimeMs":null},"openapi":{"found":false},"wellKnown":{"llmsTxt":{"found":true,"path":"/llms.txt","length":43634},"agentsJson":{"found":false},"robotsTxt":{"found":true,"blocksAgents":true,"hasSitemap":true}},"packages":{"npm":[{"name":"@pinecone-database/pinecone","description":"The official Pinecone TypeScript SDK for building vector search applications with AI/ML.","version":"7.1.0"},{"name":"@traceloop/instrumentation-pinecone","description":"OpenTelemetry instrumentation for pinecone vector DB","version":"0.22.5"},{"name":"@pinecone-database/connect","description":"Pinecone partners can easily connect their apps to Pinecone.","version":"0.0.4"},{"name":"@pinecone-database/mcp","description":"Model Context Protocol server for Pinecone - enables AI assistants to interact with Pinecone indexes and documentation","version":"0.2.1"},{"name":"@mastra/pinecone","description":"Pinecone vector store provider for Mastra","version":"1.0.1"},{"name":"firebase-tools","description":"Command-Line Interface for Firebase","version":"15.9.1"},{"name":"@langchain/pinecone","description":"LangChain integration for Pinecone's vector database","version":"1.0.1"},{"name":"@playwright/mcp","description":"Playwright Tools for MCP","version":"0.0.68"}],"pypi":[{"name":"pinecone","version":"8.1.0","description":"Pinecone client and SDK"}],"cli":false,"sdks":["Node (@pinecone-database/pinecone)","Python (pinecone)"]},"mcp":{"found":false,"type":"none","servers":[]}},"biggestFriction":"Absence of an OpenAPI specification severely limits automated API discovery and client code generation, forcing agents to rely on manual documentation and hardcoded integrations.","agentSummary":"Pinecone is well-equipped for agent use with strong programmatic access (REST API, SDKs, MCP server) and API key authentication, making it straightforward to integrate into AI systems for vector search and retrieval. The main limitation is the lack of an OpenAPI spec for discoverability and the absence of reactive mechanisms like webhooks, which reduces real-time capability."}}