Agentic AI Foundation
The Agentic AI Foundation (AAIF) is a Linux Foundation project, announced 9 December 2025, that gives the core open standards and projects of the AI agent ecosystem a neutral home. It hosts five projects: Anthropic's Model Context Protocol (MCP), Block's goose agent, OpenAI's AGENTS.md, the Agent2Agent (A2A) protocol, and agentgateway. Platinum members include Amazon Web Services, Anthropic, Block, Bloomberg, Cloudflare, Google, Microsoft and OpenAI. AAIF publishes no commercial API of its own; the callable surface in this profile belongs to its hosted projects — the Official MCP Registry REST API, a live anonymous MCP server on the MCP documentation host, and an A2A agent card served from the same host. Eight working groups cover reliability, agentic commerce, governance and regulatory alignment, identity and trust, observability, security and privacy, workflow integration, and taxonomy.
Real signal across most facets with visible, nameable gaps — the contract exists but is thin, or the portal is good while governance and commercial terms are absent.
API Evangelist profiles Agentic AI Foundation the way a machine reads it — 37 machine-readable artifacts across 6 APIs, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — Agentic AI Foundation scores 50.6/100 (developing), with a separate agent-readiness read of 29/100 (agent ready). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance. Every facet and dimension name is a link: it opens that measurement's page on APIs.io, where the rating runs across the whole catalog — the exact checks that feed it, how every profiled provider distributes on it, and who is at the top of it.
Kin Score
How this is scored →scored 2026-09-10 · rubric v0.20.0
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How we profile Agentic AI Foundation
Each block below is one kind of artifact we hold for Agentic AI Foundation. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
APIs 6
Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.
Individual APIs this provider publishes, each with its own machine-readable definition.
Model Context Protocol (MCP)
The Model Context Protocol (MCP) is an open JSON-RPC 2.0 standard for connecting AI applications to external systems — tools, resources and reusable prompt templates. Originally...
Official MCP Registry API
The Official MCP Registry is the community registry service for Model Context Protocol servers, run by the MCP project. Its REST API publishes a full OpenAPI 3.1.0 contract with...
Goose AI Agent
goose is a general-purpose, open-source AI agent that runs locally. Originally from Block and now governed by AAIF, it is written in Rust, ships a desktop app and a CLI for macO...
AGENTS.md
AGENTS.md is a simple, universal convention that gives AI coding agents a consistent, predictable source of project-specific guidance — build commands, test commands, convention...
Agent2Agent (A2A)
Agent2Agent (A2A) is an open protocol that lets AI agents built by different organisations, on different frameworks, discover each other's capabilities, communicate, delegate ta...
agentgateway
agentgateway is an open-source data plane for agentic AI — it secures, observes and governs the connections between AI agents, models, MCP tools and APIs across ecosystems. Host...
MCP Servers 1
Model Context Protocol servers expose these APIs directly to AI agents. We profile them because agent-native access is the fastest-growing way this provider's capabilities actually get used.
Model Context Protocol servers that expose these APIs to AI agents.
Agentic AI Foundation MCP Server
The Model Context Protocol project — the standard AAIF governs — operates a live, anonymous remote MCP server on its own documentation host. tools/list returned HTTP 200 with th...
MCP SERVERPricing Plans 1
Pricing is part of the interface. Machine-readable plans tell you what a tier costs and includes before you commit — one of the things the Kin Score reads for access clarity — renamed from commercial clarity in rubric 0.12, because a free statutory interface has access terms and no commercial ones.
Published pricing tiers and plan structures.
Rate Limits 1
Rate limits are the difference between a demo that works and a production integration that doesn't fall over. Publishing them is an operational-transparency signal — and a hard requirement for any agent that plans its own throughput.
Documented rate limits and quota policies.
Agentic Ai Foundation Rate Limits
RATE LIMITSFinOps 1
Cost, billing, and metering signals let a buyer model the financial operations of an API before it's live. We profile them for the same reason we profile pricing: the money is part of the contract.
Cost, billing, and metering signals for API financial operations.
Features 6
The notable capabilities this provider advertises, captured as structured features so they can be searched and compared instead of read one landing page at a time.
Notable capabilities this provider offers.
Neutral Open Governance
All AAIF projects operate under Linux Foundation neutral governance, ensuring no single vendor controls the direction of AI agent standards.
Model Context Protocol (MCP)
MCP is a universal adapter standard enabling AI agents to connect to any external tool, data source, or workflow through a consistent protocol.
Cross-Platform Agent Portability
AAIF standards enable AI agents to run consistently across different platforms, environments, and LLM providers without vendor lock-in.
Tool and Extension Ecosystem
The MCP standard enables a rich ecosystem of 70+ tools and extensions that any compliant agent can discover and invoke.
Multi-LLM Provider Support
AAIF projects support 15+ LLM providers including Anthropic, OpenAI, Google, Azure, and Ollama through standardized provider interfaces.
Open Agent Communication
The Agent Communication Protocol (ACP) enables agents to authenticate and communicate with each other and LLM providers through open standards.
Semantic Vocabularies 1
JSON-LD contexts give the data shared meaning across APIs. We profile them because semantics are what let a machine reconcile 'customer' here with 'customer' somewhere else.
JSON-LD contexts and semantic vocabularies used across these APIs.
Spectral Rules 1
Governance rulesets we run against this provider's specs — the automated checks behind parts of the score. Profiling them makes the quality bar explicit and re-runnable, not a matter of opinion.
Agentic AI Foundation API Rules
SPECTRALJSON Schema 3
Standalone JSON Schema definitions describe the data models behind the API. We profile them so the shapes are validatable on their own — useful long after a single request is forgotten.
Standalone JSON Schema definitions for this provider's data models.
JSON Structure 2
JSON Structure captures the data shapes in a form built for tooling — a complement to JSON Schema that keeps the model machine-legible.
JSON Structure definitions describing this provider's data shapes.
Agentic Ai Foundation Mcp Resource Structure
JSON STRUCTUREAgentic Ai Foundation Mcp Tool Structure
JSON STRUCTUREExamples 2
Real request and response payloads are what turn a spec from abstract into obvious — and they're one of the twelve things an agent needs to call an API correctly on the first try.
Example request and response payloads for these APIs.
Security Posture 3
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Use Cases 4
What developers actually build with this provider — captured so the catalogue answers 'what is this for', not just 'what does this expose'.
What developers build with this provider.
Interoperable AI Tool Development
Build MCP-compatible tools once and make them available to any AI agent or client that supports the MCP standard, eliminating integration silos.
Enterprise Agent Standardization
Organizations adopt AAIF standards to ensure their AI agent infrastructure is portable, auditable, and not locked to a single AI vendor.
Multi-Agent Workflow Orchestration
Use AAIF protocols to connect specialized AI agents that collaborate on complex tasks, each contributing domain-specific capabilities.
Open-Source Agent Development
Developers build and extend open-source AI agents like goose using the AAIF ecosystem of standards and extensions.
Integrations 5
Pre-built integrations with other platforms tell you where this provider already fits in a stack.
Pre-built integrations with other platforms and tools.
Claude (Anthropic)
Native MCP support via the Anthropic Messages API, the originating implementation of the MCP standard.
ChatGPT (OpenAI)
MCP tool integration via the OpenAI Responses API, enabling ChatGPT to invoke MCP-compatible tools.
VS Code
GitHub Copilot in VS Code supports MCP servers for AI-assisted development through the AAIF MCP standard.
Cursor
Cursor IDE integrates MCP tool support for AI-assisted coding agents.
Linux Foundation
AAIF operates under Linux Foundation governance alongside related projects in the LF AI & Data portfolio.
Resources
Every other property we hold for Agentic AI Foundation — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Get Started 3
Portal, sign-up, and the first successful call
Documentation 4
Reference material describing how the API behaves
Agent Surfaces 4
MCP servers, agent skills, and machine-readable catalogs
Design & Contract 8
Pagination, idempotency, versioning, errors, and events
Scroll within the panel for all 8 ·
Build 7
SDKs, sample code, and the tooling you integrate with
Scroll within the panel for all 7 ·
Access & Security 6
Authentication, authorization, and security posture
Operate 7
Status, limits, changes, and where to get help
Scroll within the panel for all 7 ·
Commercial 3
Pricing, plans, and the legal terms of use
Company 4
The organization behind the API
Other 2
Properties that don't map to a standard resource type
← All providers · Data indexed from github.com/api-evangelist/agentic-ai-foundation · machine-readable index on apis.io
This is an independent, third-party profile of Agentic AI Foundation, published by API Evangelist. We do not operate, host, resell, or support these APIs, and we are not affiliated with or endorsed by the company unless stated above. Everything here is built from publicly available information — the company's own site, developer portal, documentation, public repositories, and the specifications it publishes for public use. Nothing is obtained by breaching a system, defeating an access control, or using credentials.
The Kin Score and Agent Readiness rating are independently calculated assessments of a company's public API artifacts, scored against a published rubric. They are not certifications, endorsements, security assessments, or audits.
Corrections, re-scores, and removal are free — no partnership or purchase required, and you do not need to justify the request. A removed company is recorded as unrated, never scored zero for having asked. Acknowledgement within one business day; removal within two.
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