npm i -g @openai/codex
or brew install --cask codex
Codex CLI is a coding agent from OpenAI that runs locally on your computer.
## Summary","version":"1.15.1"},{"name":"metorial","description":"The official Node.js/TypeScript SDK for [Metorial](https://metorial.com) - Connect your AI agents to any MCP server with a single line of code. Deploy tools like Slack, GitHub, SAP, and hundreds more without managing infrastructure.","version":"2.0.2"},{"name":"@mariozechner/pi-ai","description":"Unified LLM API with automatic model discovery and provider configuration","version":"0.57.1"},{"name":"@anthropic-ai/mcpb","description":"Tools for building MCP Bundles","version":"2.1.2"}],"pypi":[{"name":"mistralai","version":"2.0.1","description":"Python Client SDK for the Mistral AI API."}],"cli":false,"sdks":["Node (@mistralai/mistralai)","Python (mistralai)"]},"mcp":{"found":false,"type":"none","servers":[]}},"biggestFriction":"The absence of an OpenAPI specification and MCP server limits agent framework discoverability and integration standardization, requiring agents to rely on SDK documentation rather than machine-readable interface contracts.","agentSummary":"Mistral AI is well-positioned for agent integration with strong authentication, streaming support, and mature SDKs across multiple languages enabling autonomous operation. However, lack of an OpenAPI spec and MCP server means agents must depend on SDK-level integration rather than standardized discovery mechanisms."}},{"_id":"69b2da6867df398baec12ea9","name":"Hugging Face","slug":"hugging-face","url":"https://huggingface.co","description":"","logo":"","category":"AI & ML","tags":[],"pricingModel":"unknown","scores":{"tokenEfficiency":{"score":8,"confidence":"scanner","evidence":"OpenAPI spec with 151 endpoints supports field selection and pagination, plus batch endpoints for efficient bulk operations, though response verbosity details are unknown.","na":false},"access":{"score":8,"confidence":"scanner","evidence":"Comprehensive REST API (151 endpoints) with official SDKs in Node.js and Python, plus integrations with major frameworks (Vercel AI SDK, ActivePieces), but lacking MCP server support limits agent interoperability.","na":false},"auth":{"score":9,"confidence":"scanner","evidence":"API key authentication enables autonomous agent operation without human intervention, with no mention of restrictive OAuth requirements or complex permission scoping needs.","na":false},"speed":{"score":6,"confidence":"scanner","evidence":"Batch endpoints and pagination support enable efficient throughput, but response time metrics are unavailable and rate limits are not documented in the collected signals.","na":false},"discoverability":{"score":9,"confidence":"scanner","evidence":"Complete OpenAPI specification available at well-known endpoint with 151 documented endpoints, developer docs present, and no apparent discovery blockers.","na":false},"reliability":{"score":6,"confidence":"scanner","evidence":"OpenAPI spec suggests consistent API structure, but signals lack evidence of idempotency keys, API versioning, response schema stability, or status page monitoring.","na":false},"safety":{"score":5,"confidence":"scanner","evidence":"API key authentication provides some access control, but no explicit evidence of sandbox/test mode, dry-run capabilities, or fine-grained scoped tokens for limiting agent permissions.","na":false},"reactivity":{"score":4,"confidence":"scanner","evidence":"No mention of webhooks, streaming, Server-Sent Events, or other real-time notification mechanisms in the collected signals.","na":false}},"agentGrade":"B+","agentScore":7.44,"accessMethods":{"restApi":true,"graphql":false,"cli":false,"sdk":["Node (@huggingface/inference)","Python (huggingface)"],"mcpServer":"none","openApiSpec":"https://huggingface.co/.well-known/openapi.json","llmsTxt":false,"agentsJson":false},"authInfo":{"methods":["api_key"],"scopedPermissions":false,"humanRequired":false},"reviewCount":0,"avgReviewScore":0,"viewCount":54,"badgeEmbedCount":15,"agentSkillSlugs":[],"alternatives":[],"claimed":false,"status":"graded","createdAt":"2026-03-12T15:23:20.706Z","updatedAt":"2026-04-09T07:47:39.832Z","__v":0,"scannerData":{"lastScannedAt":"2026-03-12T19:35:45.319Z","scanVersion":1,"rawSignals":{"homepage":{"status":200,"contentLength":145558,"hasStructuredData":false,"hasDeveloperDocs":true,"hasAgentMentions":true,"responseTimeMs":null},"openapi":{"found":true,"specUrl":"https://huggingface.co/.well-known/openapi.json","endpointCount":151,"hasFieldSelection":true,"hasPagination":true,"hasBatchEndpoints":true,"authMethods":["api_key"]},"wellKnown":{"llmsTxt":{"found":false},"agentsJson":{"found":false},"robotsTxt":{"found":true,"blocksAgents":false,"hasSitemap":true}},"packages":{"npm":[{"name":"@huggingface/inference","description":"Typescript client for the Hugging Face Inference Providers and Inference Endpoints","version":"4.13.15"},{"name":"@huggingface/hub","description":"Utilities to interact with the Hugging Face hub","version":"2.11.0"},{"name":"@theia/ai-huggingface","description":"Theia - Hugging Face Integration","version":"1.69.0"},{"name":"@logto/connector-huggingface","description":"Hugging Face connector implementation.","version":"0.4.3"},{"name":"@huggingface/space-header","description":"Use the Space mini_header outside Hugging Face","version":"1.0.4"},{"name":"@huggingface/jinja","description":"A minimalistic JavaScript implementation of the Jinja templating engine, specifically designed for parsing and rendering ML chat templates.","version":"0.5.6"},{"name":"@ai-sdk/huggingface","description":"The **[Hugging Face Inference Providers](https://huggingface.co/docs/inference-providers/index)** for the [Vercel AI SDK](https://ai-sdk.dev/docs) contains language model support for thousands of models through multiple inference providers via the Hugging","version":"1.0.37"},{"name":"@activepieces/piece-hugging-face","description":"This library was generated with [Nx](https://nx.dev).","version":"0.1.3"}],"pypi":[{"name":"huggingface","version":"0.0.1","description":"HuggingFace is a single library comprising the main HuggingFace libraries."}],"cli":false,"sdks":["Node (@huggingface/inference)","Python (huggingface)"]},"mcp":{"found":false,"type":"none","servers":[]}},"biggestFriction":"Absence of MCP server support and lack of real-time reactivity features (webhooks/streaming) limit agent framework interoperability and event-driven automation capabilities.","agentSummary":"Hugging Face offers excellent programmatic access through a comprehensive REST API with strong discoverability via OpenAPI specs and multi-language SDKs, making it well-suited for agent integration. However, the lack of MCP support, missing safety guardrails (sandbox/test mode), and no real-time reactivity features leave room for improvement in modern agent-native tooling."}},{"_id":"69b2da6867df398baec12ea3","name":"Cohere","slug":"cohere","url":"https://cohere.com","description":"","logo":"","category":"AI & ML","tags":[],"pricingModel":"unknown","scores":{"tokenEfficiency":{"score":7,"confidence":"scanner","evidence":"Cohere's API supports streaming responses and batch processing, but the collected signals lack evidence of field selection or granular response control, suggesting moderate efficiency.","na":false},"access":{"score":8,"confidence":"scanner","evidence":"Multiple official SDKs (Node, Python), integrations with AI frameworks (LangChain, Vercel AI SDK), and broad ecosystem support provide excellent programmatic access, though no MCP server is available.","na":false},"auth":{"score":8,"confidence":"scanner","evidence":"API key-based authentication is straightforward for autonomous agent use with no OAuth complexity, and Cohere's developer-focused positioning suggests proper scoping mechanisms.","na":false},"speed":{"score":7,"confidence":"scanner","evidence":"As a cloud-based API, Cohere likely has reasonable latency for inference workloads, but no explicit evidence of rate limit transparency, ETags, or concurrent request optimization in the signals.","na":false},"discoverability":{"score":7,"confidence":"scanner","evidence":"Comprehensive llms.txt file (4.8KB) and documented developer resources indicate good discoverability, but absence of OpenAPI spec is a notable gap for automatic integration.","na":false},"reliability":{"score":7,"confidence":"scanner","evidence":"Mature product with ecosystem integrations suggests stable versioning and consistent schemas, but no explicit data on idempotency support or API versioning strategy in collected signals.","na":false},"safety":{"score":6,"confidence":"scanner","evidence":"Standard API key scoping provides basic safety, but no evidence of sandbox mode, dry-run capabilities, or granular permission controls specific to agent use cases.","na":false},"reactivity":{"score":7,"confidence":"scanner","evidence":"Streaming response support for real-time inference is evident from SDK implementations, but no indication of webhooks or event-driven capabilities.","na":false}},"agentGrade":"B+","agentScore":7.26,"accessMethods":{"restApi":true,"graphql":false,"cli":false,"sdk":["Node (@ai-sdk/cohere)","Python (cohere)"],"mcpServer":"none","openApiSpec":"","llmsTxt":true,"agentsJson":false},"authInfo":{"methods":["unknown"],"scopedPermissions":false,"humanRequired":true},"reviewCount":0,"avgReviewScore":0,"viewCount":44,"badgeEmbedCount":4,"agentSkillSlugs":[],"alternatives":[],"claimed":false,"status":"graded","createdAt":"2026-03-12T15:23:20.700Z","updatedAt":"2026-04-09T04:51:33.654Z","__v":0,"scannerData":{"lastScannedAt":"2026-03-12T19:35:25.721Z","scanVersion":1,"rawSignals":{"homepage":{"status":200,"contentLength":629692,"hasStructuredData":true,"hasDeveloperDocs":true,"hasAgentMentions":true,"responseTimeMs":null},"openapi":{"found":false},"wellKnown":{"llmsTxt":{"found":true,"path":"/llms.txt","length":4801},"agentsJson":{"found":false},"robotsTxt":{"found":true,"blocksAgents":true,"hasSitemap":true}},"packages":{"npm":[{"name":"@ai-sdk/cohere","description":"The **[Cohere provider](https://ai-sdk.dev/providers/ai-sdk-providers/cohere)** for the [AI SDK](https://ai-sdk.dev/docs) contains language model support for the Cohere API.","version":"3.0.25"},{"name":"@langchain/cohere","description":"Cohere integration for LangChain.js","version":"1.0.4"},{"name":"@traceloop/instrumentation-cohere","description":"Cohere Instrumentation","version":"0.22.6"},{"name":"@memberjunction/ai-cohere","description":"MemberJunction: Cohere AI Provider - Semantic reranking using Cohere's Rerank API","version":"5.11.0"},{"name":"@chroma-core/cohere","description":"Cohere embedding provider for Chroma","version":"0.1.9"},{"name":"cohere-ai","description":"","version":"7.20.0"},{"name":"firebase-tools","description":"Command-Line Interface for Firebase","version":"15.9.1"},{"name":"@playwright/mcp","description":"Playwright Tools for MCP","version":"0.0.68"}],"pypi":[{"name":"cohere","version":"5.20.7","description":null}],"cli":false,"sdks":["Node (@ai-sdk/cohere)","Python (cohere)"]},"mcp":{"found":false,"type":"none","servers":[]}},"biggestFriction":"Absence of an OpenAPI specification and MCP server limits automatic schema discovery and integration, requiring agents to rely on documentation or SDK-specific implementations.","agentSummary":"Cohere is well-positioned for agent integration with mature SDKs, API-first design, and straightforward key-based auth, but lacks OpenAPI specs and MCP support that would enable fully autonomous discovery and integration. The platform's strong ecosystem presence and streaming capabilities make it reliable for agentic workflows despite these gaps."}},{"_id":"69b2da6867df398baec12ec4","name":"E2B","slug":"e2b","url":"https://e2b.dev","description":"","logo":"","category":"AI & ML","tags":[],"pricingModel":"unknown","scores":{"tokenEfficiency":{"score":7,"confidence":"scanner","evidence":"E2B provides structured SDK responses and stateful code execution that minimizes redundant data transmission, though no explicit field selection or response compression mechanisms are documented.","na":false},"access":{"score":8,"confidence":"scanner","evidence":"E2B offers comprehensive programmatic access through SDKs in Node.js and Python, a CLI tool, and multiple specialized packages (@e2b/code-interpreter, @e2b/desktop) enabling diverse integration patterns, though no MCP server or public REST API specification is available.","na":false},"auth":{"score":8,"confidence":"scanner","evidence":"E2B SDK supports autonomous authentication via API keys with no human-in-the-loop required, and the presence of llms.txt suggests agent-friendly documentation of authentication methods.","na":false},"speed":{"score":7,"confidence":"scanner","evidence":"E2B's cloud sandbox infrastructure and stateful code interpreter enable fast execution contexts, though no specific rate limits, caching mechanisms, or concurrent request details are documented.","na":false},"discoverability":{"score":6,"confidence":"scanner","evidence":"E2B publishes llms.txt (10KB) and maintains developer documentation, but lacks an OpenAPI spec and agents.json file, making machine-readable API discovery incomplete.","na":false},"reliability":{"score":7,"confidence":"scanner","evidence":"The stable versioning (2.x SDK versions across packages) and consistent SDK interfaces suggest reliable response schemas, though no explicit documentation of idempotency keys, versioning strategy, or status page is evident.","na":false},"safety":{"score":8,"confidence":"scanner","evidence":"E2B's core value proposition is providing isolated cloud sandbox environments for code execution, inherently offering strong safety guarantees through environment isolation and resource containment.","na":false},"reactivity":{"score":5,"confidence":"scanner","evidence":"No evidence of webhooks, streaming responses, or server-sent events in the collected signals; agents would likely rely on polling for execution status updates.","na":false}},"agentGrade":"B+","agentScore":7.22,"accessMethods":{"restApi":true,"graphql":false,"cli":true,"sdk":["Node (e2b)","Python (e2b)"],"mcpServer":"none","openApiSpec":"","llmsTxt":true,"agentsJson":false},"authInfo":{"methods":["unknown"],"scopedPermissions":false,"humanRequired":true},"reviewCount":0,"avgReviewScore":0,"viewCount":31,"badgeEmbedCount":5,"agentSkillSlugs":[],"alternatives":[],"claimed":false,"status":"graded","createdAt":"2026-03-12T15:23:20.712Z","updatedAt":"2026-04-02T05:29:23.770Z","__v":0,"scannerData":{"lastScannedAt":"2026-03-12T19:37:36.524Z","scanVersion":1,"rawSignals":{"homepage":{"status":200,"contentLength":937180,"hasStructuredData":false,"hasDeveloperDocs":true,"hasAgentMentions":true,"responseTimeMs":null},"openapi":{"found":false},"wellKnown":{"llmsTxt":{"found":true,"path":"/llms.txt","length":10262},"agentsJson":{"found":false},"robotsTxt":{"found":true,"blocksAgents":false,"hasSitemap":true}},"packages":{"npm":[{"name":"e2b","description":"E2B SDK that give agents cloud environments","version":"2.14.1"},{"name":"@e2b/code-interpreter","description":"E2B Code Interpreter - Stateful code execution","version":"2.3.3"},{"name":"@e2b/cli","description":"CLI for managing e2b sandbox templates","version":"2.8.1"},{"name":"@e2b/desktop","description":"E2B Desktop Sandbox - isolated cloud environment with a desktop-like interface powered by E2B. Ready for AI Computer Use","version":"2.2.2"},{"name":"@mastra/e2b","description":"E2B cloud sandbox provider for Mastra workspaces","version":"0.1.2"},{"name":"firebase-tools","description":"Command-Line Interface for Firebase","version":"15.9.1"},{"name":"@playwright/mcp","description":"Playwright Tools for MCP","version":"0.0.68"}],"pypi":[{"name":"e2b","version":"2.15.2","description":"E2B SDK that give agents cloud environments"}],"cli":true,"sdks":["Node (e2b)","Python (e2b)"]},"mcp":{"found":false,"type":"none","servers":[]}},"biggestFriction":"Lack of a public OpenAPI specification and REST API documentation makes it difficult for agents to discover available operations without tight SDK coupling.","agentSummary":"E2B is a purpose-built platform for giving agents isolated cloud execution environments, with strong safety through sandboxing and solid SDK support across Node.js and Python. However, missing OpenAPI specs and MCP server support limits discoverability and reactivity compared to best-in-class tools."}},{"_id":"69b2da6867df398baec12eaf","name":"Groq","slug":"groq","url":"https://groq.com","description":"","logo":"","category":"AI & ML","tags":[],"pricingModel":"unknown","scores":{"tokenEfficiency":{"score":7,"confidence":"scanner","evidence":"As an LLM API, Groq likely supports streaming and reasonable response controls, but without visible OpenAPI spec or detailed documentation signals, token optimization patterns are not fully verifiable.","na":false},"access":{"score":8,"confidence":"scanner","evidence":"Official SDKs in Node.js and Python, LangChain integration, and Vercel AI SDK support provide multiple programmatic access paths; absence of MCP server and OpenAPI spec prevents a higher score.","na":false},"auth":{"score":9,"confidence":"scanner","evidence":"API key authentication is standard for LLM providers and enables autonomous agent authentication without human-in-the-loop or OAuth complexity; ideal for agent use cases.","na":false},"speed":{"score":8,"confidence":"scanner","evidence":"Groq is specifically positioned as a high-speed inference engine with lower latency than competitors; lack of published rate limits and response time data in collected signals prevents a perfect score.","na":false},"discoverability":{"score":5,"confidence":"scanner","evidence":"Developer docs are present but no OpenAPI spec, llms.txt, or agents.json detected; documentation quality cannot be fully assessed from collected signals alone.","na":false},"reliability":{"score":6,"confidence":"scanner","evidence":"Standard API provider but no explicit signals for idempotency keys, versioning strategy, or consistent schemas; typical SaaS reliability assumed but not confirmed.","na":false},"safety":{"score":6,"confidence":"scanner","evidence":"API key scoping is available for permission control, but no evidence of sandbox/test mode, dry-run capabilities, or operation undo mechanisms in collected signals.","na":false},"reactivity":{"score":7,"confidence":"scanner","evidence":"LLM APIs typically support streaming responses for real-time token delivery; webhooks or polling mechanisms not indicated in collected signals but streaming is strongly implied.","na":false}},"agentGrade":"B+","agentScore":7.2,"accessMethods":{"restApi":true,"graphql":false,"cli":false,"sdk":["Node (groq)","Python (groq)"],"mcpServer":"none","openApiSpec":"","llmsTxt":false,"agentsJson":false},"authInfo":{"methods":["unknown"],"scopedPermissions":false,"humanRequired":true},"reviewCount":0,"avgReviewScore":0,"viewCount":32,"badgeEmbedCount":5,"agentSkillSlugs":[],"alternatives":[],"claimed":false,"status":"graded","createdAt":"2026-03-12T15:23:20.708Z","updatedAt":"2026-04-09T07:45:48.385Z","__v":0,"scannerData":{"lastScannedAt":"2026-03-12T19:36:04.661Z","scanVersion":1,"rawSignals":{"homepage":{"status":200,"contentLength":1313284,"hasStructuredData":false,"hasDeveloperDocs":true,"hasAgentMentions":true,"responseTimeMs":null},"openapi":{"found":false},"wellKnown":{"llmsTxt":{"found":false},"agentsJson":{"found":false},"robotsTxt":{"found":true,"blocksAgents":false,"hasSitemap":true}},"packages":{"npm":[{"name":"groq","description":"Tagged template literal for Sanity.io GROQ-queries","version":"5.14.1"},{"name":"@langchain/groq","description":"Groq integration for LangChain.js","version":"1.1.5"},{"name":"groq-js","description":"[](https://npm-stat.com/charts.html?package=groq-js) [](https://www.npmjs.com/package/groq-js) [![gzip size][","version":"1.29.0"},{"name":"groq-sdk","description":"The official TypeScript library for the Groq API","version":"1.1.1"},{"name":"@ai-sdk/groq","description":"The **[Groq provider](https://ai-sdk.dev/providers/ai-sdk-providers/groq)** for the [AI SDK](https://ai-sdk.dev/docs) contains language model support for the Groq chat and completion APIs, transcription support, and browser search tool.","version":"3.0.29"},{"name":"firebase-tools","description":"Command-Line Interface for Firebase","version":"15.9.1"}],"pypi":[{"name":"groq","version":"1.1.1","description":"The official Python library for the groq API"}],"cli":false,"sdks":["Node (groq)","Python (groq)"]},"mcp":{"found":false,"type":"none","servers":[]}},"biggestFriction":"Absence of OpenAPI spec, llms.txt, and agents.json makes it harder for agents to discover API capabilities, requirements, and integration patterns compared to more agent-optimized platforms.","agentSummary":"Groq is well-positioned for agent use with excellent speed characteristics, multiple SDK options, and API-key authentication that enables autonomous operation. However, lack of formal API documentation standards (OpenAPI, agents.json) and MCP server support leaves some discoverability gaps that could slow agent integration."}},{"_id":"69b2da6867df398baec12ebe","name":"Deepgram","slug":"deepgram","url":"https://deepgram.com","description":"","logo":"","category":"AI & ML","tags":[],"pricingModel":"unknown","scores":{"tokenEfficiency":{"score":7,"confidence":"scanner","evidence":"Deepgram's API appears to support efficient transcription/TTS operations with structured request/response patterns, though without an OpenAPI spec available, exact field selection and pagination capabilities cannot be fully verified.","na":false},"access":{"score":8,"confidence":"scanner","evidence":"Multiple official SDKs available (Node.js via @deepgram/sdk v5.0.0, AI SDK integration, LiveKit, Mastra), strong third-party integrations (n8n), and developer documentation present, though no MCP server or GraphQL option limits top-tier scoring.","na":false},"auth":{"score":8,"confidence":"scanner","evidence":"API key-based authentication is standard for Deepgram's services, enabling autonomous agent authentication without OAuth friction, and the /llms.txt file suggests awareness of agent use cases with likely API key guidance.","na":false},"speed":{"score":7,"confidence":"scanner","evidence":"As a speech/transcription service, Deepgram supports streaming APIs (indicated by agent mentions and typical industry patterns), though specific rate limits, latency SLAs, and concurrent request handling details are not disclosed in available signals.","na":false},"discoverability":{"score":6,"confidence":"scanner","evidence":"Developer documentation and /llms.txt file are present with good intent for agent discoverability, but the absence of an OpenAPI spec and robots.txt blocking agent crawling create friction for automated API discovery and schema introspection.","na":false},"reliability":{"score":7,"confidence":"scanner","evidence":"SDK versioning (5.0.0) and consistent integration patterns suggest stable APIs, though without visible versioning headers, idempotency guarantees, or public status page information, full reliability assessment is limited.","na":false},"safety":{"score":6,"confidence":"scanner","evidence":"API key scoping is likely supported (standard for speech APIs), but no explicit evidence of sandbox mode, test keys, dry-run capabilities, or detailed permission scoping is visible in the collected signals.","na":false},"reactivity":{"score":8,"confidence":"scanner","evidence":"Deepgram supports streaming transcription and real-time speech processing (core to its product), enabling efficient polling and real-time agent interactions, though webhook support for async operations is not explicitly confirmed.","na":false}},"agentGrade":"B+","agentScore":7.18,"accessMethods":{"restApi":true,"graphql":false,"cli":false,"sdk":["Node (@ai-sdk/deepgram)"],"mcpServer":"none","openApiSpec":"","llmsTxt":true,"agentsJson":false},"authInfo":{"methods":["unknown"],"scopedPermissions":false,"humanRequired":true},"reviewCount":0,"avgReviewScore":0,"viewCount":35,"badgeEmbedCount":3,"agentSkillSlugs":[],"alternatives":[],"claimed":false,"status":"graded","createdAt":"2026-03-12T15:23:20.711Z","updatedAt":"2026-04-08T16:55:11.307Z","__v":0,"scannerData":{"lastScannedAt":"2026-03-12T19:37:06.923Z","scanVersion":1,"rawSignals":{"homepage":{"status":200,"contentLength":475578,"hasStructuredData":false,"hasDeveloperDocs":true,"hasAgentMentions":true,"responseTimeMs":null},"openapi":{"found":false},"wellKnown":{"llmsTxt":{"found":true,"path":"/llms.txt","length":3384},"agentsJson":{"found":false},"robotsTxt":{"found":true,"blocksAgents":true,"hasSitemap":true}},"packages":{"npm":[{"name":"@ai-sdk/deepgram","description":"The **[Deepgram provider](https://ai-sdk.dev/providers/ai-sdk-providers/deepgram)** for the [AI SDK](https://ai-sdk.dev/docs) contains transcription model support for the Deepgram transcription API and speech model support for the Deepgram text-to-speech ","version":"2.0.24"},{"name":"@deepgram/sdk","description":" [](https://www.npmjs.com/package/@deepgram/sdk) [![Node.js 18+](https://img.shields.io/badge/node-18+-b","version":"5.0.0"},{"name":"@livekit/agents-plugin-deepgram","description":"Deepgram plugin for LiveKit Agents for Node.js","version":"1.0.50"},{"name":"@mastra/voice-deepgram","description":"Mastra Deepgram voice integration","version":"0.12.0"},{"name":"n8n-nodes-deepgram","description":"n8n community nodes for Deepgram","version":"0.2.7"},{"name":"@deepgram/captions","description":"Node implementation of Deepgram's WebVTT and SRT formatting. Given a transcription, this package can return a valid string to store as WebVTT or SRT caption files.","version":"1.2.0"},{"name":"firebase-tools","description":"Command-Line Interface for Firebase","version":"15.9.1"},{"name":"@playwright/mcp","description":"Playwright Tools for MCP","version":"0.0.68"}],"pypi":[],"cli":false,"sdks":["Node (@ai-sdk/deepgram)"]},"mcp":{"found":false,"type":"none","servers":[]}},"biggestFriction":"Absence of an OpenAPI specification prevents automated schema discovery and makes it harder for agents to self-serve API integration without manual documentation consultation.","agentSummary":"Deepgram is well-positioned for agent integration with strong SDK support, streaming capabilities, and API key authentication, making it suitable for voice-enabled agentic applications. However, the lack of an OpenAPI spec and explicit agent-friendly tooling (like MCP servers) requires agents to rely on documentation-based integration rather than automated schema discovery."}},{"_id":"69b2da6867df398baec12ea6","name":"Replicate","slug":"replicate","url":"https://replicate.com","description":"","logo":"","category":"AI & ML","tags":[],"pricingModel":"unknown","scores":{"tokenEfficiency":{"score":6,"confidence":"scanner","evidence":"Replicate's API supports pagination and field selection for listings, but image/model execution responses can be large and verbose with full metadata; no explicit batching API endpoint documented.","na":false},"access":{"score":8,"confidence":"scanner","evidence":"Replicate offers REST API, official Node.js and Python SDKs, an MCP server (replicate-mcp), and mentions agent support on the homepage, providing excellent programmatic access breadth.","na":false},"auth":{"score":8,"confidence":"scanner","evidence":"Replicate uses API keys for authentication which are autonomously usable by agents without human intervention, though token scoping details and permission granularity are not explicitly documented in the signals.","na":false},"speed":{"score":7,"confidence":"scanner","evidence":"As a cloud-based ML service, Replicate handles asynchronous model execution well, but actual model inference latency depends on infrastructure and queue times; rate limits appear reasonable for an API service but specifics are not documented.","na":false},"discoverability":{"score":7,"confidence":"scanner","evidence":"Replicate publishes an llms.txt file and has developer documentation, but no OpenAPI spec was found; the homepage mentions agent support and has structured content, suggesting reasonable discoverability.","na":false},"reliability":{"score":7,"confidence":"scanner","evidence":"The official TypeScript library (replicate-stainless) and versioned SDKs suggest API versioning and stability, but idempotency key support and explicit status page information are not evident in the signals.","na":false},"safety":{"score":6,"confidence":"scanner","evidence":"Replicate supports running predictions on specific model versions providing some control, but no explicit sandbox/test mode, dry-run capabilities, or detailed scoping documentation is evident; API key restrictions appear limited.","na":false},"reactivity":{"score":7,"confidence":"scanner","evidence":"Replicate's async prediction model supports webhooks for completion notifications and streaming output for real-time results, enabling good reactive patterns for long-running model inference tasks.","na":false}},"agentGrade":"B+","agentScore":7.06,"accessMethods":{"restApi":true,"graphql":false,"cli":false,"sdk":["Node (replicate)","Python (replicate)"],"mcpServer":"none","openApiSpec":"","llmsTxt":true,"agentsJson":false},"authInfo":{"methods":["unknown"],"scopedPermissions":false,"humanRequired":true},"reviewCount":0,"avgReviewScore":0,"viewCount":40,"badgeEmbedCount":5,"agentSkillSlugs":[],"alternatives":[],"claimed":false,"status":"graded","createdAt":"2026-03-12T15:23:20.701Z","updatedAt":"2026-04-09T07:43:08.652Z","__v":0,"scannerData":{"lastScannedAt":"2026-03-12T19:35:36.335Z","scanVersion":1,"rawSignals":{"homepage":{"status":200,"contentLength":350170,"hasStructuredData":false,"hasDeveloperDocs":true,"hasAgentMentions":true,"responseTimeMs":null},"openapi":{"found":false},"wellKnown":{"llmsTxt":{"found":true,"path":"/llms.txt","length":3416},"agentsJson":{"found":false},"robotsTxt":{"found":true,"blocksAgents":false,"hasSitemap":true}},"packages":{"npm":[{"name":"replicate","description":"JavaScript client for Replicate","version":"1.4.0"},{"name":"@ai-sdk/replicate","description":"The **[Replicate provider](https://ai-sdk.dev/providers/ai-sdk-providers/replicate)** for the [AI SDK](https://ai-sdk.dev/docs) contains image model support for the Replicate API.","version":"2.0.24"},{"name":"replicate-stainless","description":"The official TypeScript library for the Replicate API","version":"0.9.0"},{"name":"babel-plugin-require-context-hook","description":"Babel plugin to replicate Webpack require.context","version":"1.0.0"},{"name":"@storybook/babel-plugin-require-context-hook","description":"Babel plugin to replicate Webpack require.context","version":"1.0.1"},{"name":"firebase-tools","description":"Command-Line Interface for Firebase","version":"15.9.1"},{"name":"replicate-mcp","description":"Official MCP server for Replicate's HTTP API","version":"0.9.0"},{"name":"@playwright/mcp","description":"Playwright Tools for MCP","version":"0.0.68"},{"name":"mcp-framework","description":"Framework for building Model Context Protocol (MCP) servers in Typescript","version":"0.2.18"}],"pypi":[{"name":"replicate","version":"1.0.7","description":"Python client for Replicate"}],"cli":false,"sdks":["Node (replicate)","Python (replicate)"]},"mcp":{"found":false,"type":"none","servers":[]}},"biggestFriction":"The absence of an OpenAPI specification makes it harder for agents to auto-discover the full API surface and validate requests, requiring manual integration with the SDKs.","agentSummary":"Replicate is well-positioned for agent use with official SDKs, MCP server support, REST API, and async/webhook capabilities for ML workloads. The main limitation is lack of formal OpenAPI documentation and limited visibility into permission scoping for autonomously safe agent operation."}},{"_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":25,"badgeEmbedCount":5,"agentSkillSlugs":[],"alternatives":[],"claimed":false,"status":"graded","createdAt":"2026-03-12T15:23:20.709Z","updatedAt":"2026-04-08T21:30:54.580Z","__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."}}]}