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Agentic AI Foundation website screenshot

Agentic AI Foundation

The Agentic AI Foundation (AAIF) is a Linux Foundation project formed in December 2025 that brings together critical open standards and projects for AI agents under neutral governance. It hosts Anthropic's Model Context Protocol (MCP), Block's goose AI agent, and OpenAI's AGENTS.md to enable interoperable, open AI agent ecosystems. The foundation drives standardization of agent communication protocols, tool interfaces, and cross-platform agent portability.

human only

Limited machine-readable signal and partial portal coverage — documentation a human can read, but little a machine or agent can consume without scraping.

Kin Score

API Evangelist profiles Agentic AI Foundation the way a machine reads it — 29 machine-readable artifacts across 2 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 36.8/100 (thin), with a separate agent-readiness read of 7/100 (human only). 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.

Kin Score Kin Score How this is scored →
scored 2026-07-27 · rubric v0.5
Composite quality — 36.8/100 · thin
Contract Quality 3.8 / 25
Developer Ergonomics 3.5 / 20
Commercial Clarity 7.9 / 20
Operational Transparency 4.8 / 13
Governance 8.8 / 12
Discoverability 8.0 / 10
Agent readiness — 7/100 · human only
Machine-Readable Contract 0 / 18
Agentic Access Contract 0 / 15
MCP Server 0 / 12
Machine-Readable Auth 0 / 10
Idempotency 0 / 9
Stable Error Semantics 0 / 8
Request/Response Examples 0 / 7
Rate-Limit Signaling 7 / 7
Typed Event Surface 0 / 6
Agent Skills 0 / 5
Well-Known Catalog 0 / 4
Consent & Bot Identity 0 / 3

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 2

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-source standard for connecting AI applications to external systems including data sources, tools, and workflows. Originally developed...

Goose AI Agent

Goose is a general-purpose, open-source AI agent that runs locally on your machine. Originally from Block and now under AAIF governance, goose supports 15+ LLM providers, 70+ MC...

Pricing 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 six things the Kin Score reads for commercial clarity.

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.

FinOps 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.

Agentic Ai Foundation Context

5 classes · 7 properties

JSON-LD

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

5 rules · 3 warnings

SPECTRAL

JSON Schema 2

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.

MCPResource

5 properties

JSON SCHEMA

MCPTool

4 properties

JSON SCHEMA

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.

Examples 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 1

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.

Agentic Ai Foundation Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

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 1

Portal, sign-up, and the first successful call

Documentation 1

Reference material describing how the API behaves

Build 1

SDKs, sample code, and the tooling you integrate with

Access & Security 1

Authentication, authorization, and security posture

Company 1

The organization behind the API

← All providers · Data indexed from github.com/api-evangelist/agentic-ai-foundation · machine-readable index on apis.io