Agent Skills

A collection of resources, APIs, and standards related to AI agent skills and capabilities. Agent skills represent the tools, functions, and capabilities that AI agents can invoke to accomplish tasks — spanning web search, code execution, file management, memory, and external API integrations. This topic covers the major platforms and frameworks that define how agent skills are declared, discovered, and invoked.

Agent Skills is listed with 3 APIs on the APIs.io network. Tagged areas include Agent Skills, AI Agents, Tool Use, Function Calling, and MCP.

The Agent Skills catalog on APIs.io includes 1 JSON-LD context and 1 Spectral governance ruleset.

Agent Skills’ developer surface includes engineering blog and 16 more developer resources.

📡 Profile data on GitHub

28.2/100 thin ▬ flat Agent 1/100 human only self hosted · Apache-2.0 Full breakdown ↓
scored 2026-11-06 · rubric v0.27.0 · Show this score on your site →

AccessFreemium

3 APIs 6 Features 6 Use Cases

Agent Skills AI Agents Tool Use Function Calling MCP Automation

Kin Score

Kin Score How this is scored →
scored 2026-11-06 · rubric v0.27.0

Composite quality — 28.2/100 · thin

Contract Quality 2.8 / 19

Developer Ergonomics 4.7 / 15

Access Clarity 1.2 / 15

Operational Transparency 2.6 / 10

Contract Governance 2.1 / 9

Discoverability 5.4 / 8

Regulatory Posture 1.6 / 15

Open Source Surface 6.5 / 10

Agent readiness — 1/100 · human only

Machine-Readable Contract 0 / 18

Agentic Access Contract 0 / 10

Documented Reversibility 0 / 6

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

A2A Agent Card 0 / 8

Dry-Run / Simulate Mode 0 / 4

Delegated User Identity 0 / 6

Protected Resource Metadata 0 / 5

Registration Without a Human 0 / 6

Agentic Commerce Well-Known Document 0 / 5

Pagination 5 / 5

Distinct Operations 3 / 3

MCP Tool Quality 6 / 6

Regulatory Posture applies to this provider. Its tags matched the Horizontal (data, software, accessibility, platform) regime, so Regulatory Posture carries 15 points of the composite. If this regime is wrong for your business, say so on your provider repo — the applicability map is public and we will correct it.

Open Source Surface applies to this provider. This product is open source and we read its repository directly, so Open Source Surface carries 10 points of the composite. It is scored from what the repository actually publishes — a security policy, a contribution guide, a release history, a code of conduct — read live from the provider rather than inferred from our own catalog pointers. This facet adds; nothing was taken away to make room for it. An open-source project is not excused from the commercial facets, because exemption would strip it of the points it does earn. If we have the wrong repository, or this product is not open source, say so on your provider repo and we will drop the facet rather than have you publish against it.

Create-or-Update Ergonomics could not be measured. We hold no machine-readable contract for this provider to read, so there is nothing to measure a write surface against. Excluded rather than scored zero: never-measured and measured-empty are different facts. Publishing an OpenAPI is what makes this facet — and several others — scorable at all.

The six quality facets above are damped to 75 points between them, because both conditional facets apply and carry 25 points together. That is why each facet's contribution is shown against a damped maximum: raising a quality facet moves the composite by 75% of its nominal weight, not 100%. The full arithmetic is at apis.io/rating/.

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Agent readiness — checks not met, largest first (exact, from this profile):

Machine-Readable Contract +18 pts — api_specs includes an OpenAPI document
MCP Server +12 pts — deployment.mode + probe verdict on the mcp/ manifest, not the pointer; an unprobed slug falls back to authorship
Agentic Access Contract +10 pts — agentic_access artifact present (x-agentic-access), graded by author
Machine-Readable Auth +10 pts — a served openid-configuration/oauth-authorization-server carrying `issuer` (served), else the securitySchemes class in the provider OpenAPIs — bound (mutualTLS, DPoP, Signature) > negotiable (oauth2 authorizationCode/clientCredentials, openIdConnect) > bearer (http bearer, apiKey)
Idempotency +9 pts — OpenAPI mutating operations declare an idempotency key — a header, a request-body property, or a named de-duplication token whose own description says so (verified); else common[].type matches /idempoten/i (documented)
Stable Error Semantics +8 pts — OpenAPI $refs a shared error schema across 4xx/5xx responses (verified), else common[].type includes ErrorCatalog / ErrorCodes / DeclineCodes

12 more unmet checks in the Agent Readiness table above.

Kin Score — facets with the most composite points still available (weight × headroom):

Contract Quality scores 14.7 — up to +21.3 composite points
Access Clarity scores 7.9 — up to +18.4 composite points
Developer Ergonomics scores 31.0 — up to +13.8 composite points

Each check links to what satisfies it. Publish the artifact, or tell us where it already lives, and the score moves on the next scoring run.

Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. Every facet and dimension name above is a link: it opens that measurement's own page — what it means, the exact checks that feed it, how the whole catalog distributes on it, and the providers at the top of it. This rating is computed from the provider profile at artifacts.apis.io/agent-skills/apis.yml: open an issue on github.com/api-evangelist/agent-skills to ask a question or point us at an artifact you publish. Submit an artifact on GitHub — free → Manage your own listing — the Influence plan, $499/mo →

Standards implemented 2

Standards this provider implements or carries. De facto ones are profiled and measured by the API Commons; formal ones come from the standards directory and are linked by the provider's own tags. The evidence column says how each claim was established — not that it was made.

Agent Skills

tagged — the provider's own APIs.json carries the standard as a tag; implementation not verified

Model Context Protocol (MCP)

tagged — the provider's own APIs.json carries the standard as a tag; implementation not verified

APIs 3

Individual APIs this provider publishes, each with its own machine-readable definition.

Anthropic Tool Use API

The Anthropic Tool Use API allows AI agents built on Claude to call client-defined functions or Anthropic-provided server tools such as web search, code execution, and web fetch...

Google Agent Development Kit (ADK)

Google's Agent Development Kit (ADK) is a flexible framework for building AI agents and multi-agent systems. It supports LLM agents, workflow agents, and custom agents with capa...

Model Context Protocol (MCP)

The Model Context Protocol (MCP) is an open-source standard for connecting AI applications to external systems. MCP defines a standardized way for AI agents to access data sourc...

Agent Skills API pricing

Not yet checked against Agent Skills's own pricing page. Compiled by API Evangelist on 2026-05-04; confirm before you rely on a figure. A free plan or tier is listed.

Plan	Price
Free freemium	Free
Professional paid	Free
Enterprise enterprise	—

Plan details, limits and sources →

Rate limits

Free Tier Default: 10 requests per minute
Free Tier Monthly Quota: 1000 requests per month
Professional Tier Default: 100 requests per minute

Compiled by API Evangelist, not yet checked against Agent Skills's documentation. All 5 limits →

Pricing Plans 1

Published pricing tiers and plan structures.

Agent Skills Plans Pricing

3 plans

PLANS

Rate Limits 1

Documented rate limits and quota policies.

Agent Skills Rate Limits

5 limits

RATE LIMITS

FinOps 1

Cost, billing, and metering signals for API financial operations.

Agent Skills Finops

FINOPS

Features 6

Notable capabilities this provider offers.

Function Calling

AI agents can invoke user-defined or platform-provided functions based on natural language instructions, with structured input/output schemas.

Server-Side Tool Execution

Platforms like Anthropic and OpenAI run certain agent skills (web search, code execution) on their own infrastructure, removing the need for client-side execution.

MCP Integration

The Model Context Protocol provides a universal adapter layer enabling agents to discover and call any MCP-compatible server as a skill.

Multi-Agent Orchestration

Frameworks like Google ADK support coordinating multiple specialized agents, with skills delegated across agent boundaries via protocols like A2A.

Strict Schema Enforcement

Agent skill definitions can enforce strict JSON Schema compliance to ensure agents produce well-formed tool calls matching the declared parameter schema.

Tool Discovery

Anthropic's tool_search server tool enables agents to discover available tools at runtime without statically declaring all tool schemas upfront.

Semantic Vocabularies 1

JSON-LD contexts and semantic vocabularies used across these APIs.

Agent Skills Context

7 classes · 16 properties

JSON-LD

Spectral Rules 1

Spectral governance rulesets for linting and validating these APIs.

Agent Skills — jsonschema rules

5 rules · 4 warnings 1 info

SPECTRAL

JSON Schema 4

Standalone JSON Schema definitions for this provider's data models.

MCPServer

6 properties

JSON SCHEMA

ToolCall

4 properties

JSON SCHEMA

ToolResult

4 properties

JSON SCHEMA

Tool

5 properties

JSON SCHEMA

JSON Structure 4

JSON Structure definitions describing this provider's data shapes.

Agent Skills Mcp Server Structure

6 properties

JSON STRUCTURE

Agent Skills Tool Call Structure

4 properties

JSON STRUCTURE

Agent Skills Tool Result Structure

4 properties

JSON STRUCTURE

Agent Skills Tool Structure

5 properties

JSON STRUCTURE

Examples 4

Example request and response payloads for these APIs.

Agent Skills Mcp Server Example

6 fields

EXAMPLE

Agent Skills Tool Call Example

4 fields

EXAMPLE

Agent Skills Tool Example

5 fields

EXAMPLE

Agent Skills Tool Result Example

4 fields

EXAMPLE

Security Posture 1

Authentication, domain security, vulnerability disclosure, and trust-center signals.

Agent Skills Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Use Cases 6

What developers build with this provider.

Automated Research

Agents use web search and fetch skills to retrieve, synthesize, and summarize information from the internet in response to user queries.

Code Generation and Execution

Agents invoke code execution skills to write, run, and debug code within sandboxed environments, returning results to the user.

Data Integration

Agents use OpenAPI-backed skills to read and write data across enterprise systems — CRMs, ERPs, databases — through standardized API calls.

File and Document Management

Agents invoke file system skills to read, write, and organize documents, images, and structured data on behalf of users.

Multi-Step Workflow Automation

Agents chain multiple skills in sequence — searching, retrieving, transforming, and storing data — to complete complex multi-step tasks autonomously.

AI-Assisted Customer Support

Customer service agents use CRM lookup, ticketing, and knowledge base skills to resolve customer issues without human escalation.

Integrations 7

Pre-built integrations with other platforms and tools.

Claude (Anthropic)

Native support for tool use and MCP via the Anthropic Messages API.

ChatGPT (OpenAI)

Function calling and MCP tool integration via the OpenAI Responses API.

Gemini (Google)

Tool use and ADK integration for Gemini-based agents.

VS Code Copilot

GitHub Copilot supports MCP servers as agent skill providers within the VS Code development environment.

Cursor

Cursor IDE supports MCP tool integration for AI-assisted coding agents.

LangChain

Open-source framework for composing agent skills into chains and graphs across multiple LLM providers.

LlamaIndex

Data framework enabling agents to index and retrieve from external data sources as structured skills.

Scroll for all 7

Resources

Documentation 5

Reference material describing how the API behaves

ContributionGuide

ContributionGuide

Tool Schema

JSONSchema

Tool Call Schema

JSONSchema

Tool Result Schema

JSONSchema

MCP Server Schema

JSONSchema

Design & Contract 2

Pagination, idempotency, versioning, errors, and events

JSONLD

JSONLD

Vocabulary

Vocabulary

Build 2

SDKs, sample code, and the tooling you integrate with

CodeOfConduct

CodeOfConduct

GitHubOrganization

GitHubOrganization

Access & Security 1

Authentication, authorization, and security posture

DomainSecurity

DomainSecurity

Operate 3

Status, limits, changes, and where to get help

RateLimits

RateLimits

IssueTracker

IssueTracker

Releases

Releases

Commercial 3

Pricing, plans, and the legal terms of use

Plans

Plans

FinOps

FinOps

License

License

Company 1

The organization behind the API

Blog

Blog

Source (apis.yml)

deliveryModel:
  model: self-hosted
  license: Apache-2.0
  open_source: true
  commercial: false
  callable_host: false
  label: Self-hosted open source · you run it yourself
  confidence: high
  source:
  - license
  generated: '2026-08-28'
  method: derived
accessModel:
  pricing: freemium
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Freemium
  confidence: low
  source:
  - plans
  - security
  generated: '2026-11-06'
  method: derived
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/agent-skills.png
name: Agent Skills
description: A collection of resources, APIs, and standards related to AI agent skills and capabilities. Agent skills represent
  the tools, functions, and capabilities that AI agents can invoke to accomplish tasks — spanning web search, code execution,
  file management, memory, and external API integrations. This topic covers the major platforms and frameworks that define
  how agent skills are declared, discovered, and invoked.
url: https://github.com/api-evangelist/agent-skills
created: '2025-01-01'
modified: '2026-10-06'
specificationVersion: '0.23'
tags:
- Agent Skills
- AI Agents
- Tool Use
- Function Calling
- MCP
- Automation
tags_raw:
- Agent Skills
- AI Agents
- Tool Use
- Function Calling
- MCP
- Agentic AI
- Automation
apis:
- name: Anthropic Tool Use API
  description: The Anthropic Tool Use API allows AI agents built on Claude to call client-defined functions or Anthropic-provided
    server tools such as web search, code execution, and web fetch. Tools are declared in the API request and Claude decides
    when to invoke them based on context. Server tools run on Anthropic infrastructure while client tools execute in the calling
    application.
  humanURL: https://platform.claude.com/docs/en/docs/agents-and-tools/tool-use/overview
  baseURL: https://api.anthropic.com
  tags:
  - Anthropic
  - Tool Use
  - Function Calling
  - Claude
  - AI Agents
  properties:
  - type: Documentation
    url: https://platform.claude.com/docs/en/docs/agents-and-tools/tool-use/overview
  - type: APIReference
    url: https://platform.claude.com/docs/en/docs/agents-and-tools/tool-use/tool-reference
  - type: GettingStarted
    url: https://platform.claude.com/docs/en/docs/agents-and-tools/tool-use/build-a-tool-using-agent
- name: Google Agent Development Kit (ADK)
  description: Google's Agent Development Kit (ADK) is a flexible framework for building AI agents and multi-agent systems.
    It supports LLM agents, workflow agents, and custom agents with capabilities including MCP tool integration, OpenAPI tools,
    function tools, grounding via Google Search, streaming via Gemini Live API, and Agent-to-Agent (A2A) protocol for inter-agent
    communication. Available in Python, TypeScript, Go, and Java.
  humanURL: https://adk.dev/
  baseURL: https://adk.dev
  tags:
  - Google
  - Agent Development Kit
  - ADK
  - Multi-Agent
  - Gemini
  - Tool Use
  properties:
  - type: Documentation
    url: https://adk.dev/
  - type: GettingStarted
    url: https://adk.dev/get-started
  - type: GitHubRepository
    url: https://github.com/google/adk-python
- name: Model Context Protocol (MCP)
  description: The Model Context Protocol (MCP) is an open-source standard for connecting AI applications to external systems.
    MCP defines a standardized way for AI agents to access data sources, tools, and workflows. It enables agents to call external
    tools, access files, databases, and APIs through a consistent protocol supported by Claude, ChatGPT, VS Code, Cursor,
    and many other AI clients.
  humanURL: https://modelcontextprotocol.io/introduction
  baseURL: https://modelcontextprotocol.io
  tags:
  - MCP
  - Standards
  - Tool Use
  - Open Source
  tags_raw:
  - MCP
  - Model Context Protocol
  - Standards
  - Tool Use
  - Open Source
  - Open-Source
  properties:
  - type: Documentation
    url: https://modelcontextprotocol.io/introduction
  - type: GettingStarted
    url: https://modelcontextprotocol.io/docs/develop/build-server
  - type: GitHubRepository
    url: https://github.com/modelcontextprotocol/specification
common:
- type: Plans
  url: plans/agent-skills-plans-pricing.yml
- type: RateLimits
  url: rate-limits/agent-skills-rate-limits.yml
- type: FinOps
  url: finops/agent-skills-finops.yml
- url: https://claude.com/resources/articles
  type: Blog
- type: IssueTracker
  url: https://github.com/google/adk-python/issues
- type: Releases
  url: https://github.com/google/adk-python/releases
- type: CodeOfConduct
  url: https://github.com/google/.github/blob/master/CODE_OF_CONDUCT.md
- type: ContributionGuide
  url: https://github.com/google/adk-python/blob/main/CONTRIBUTING.md
- type: License
  name: Apache-2.0
  url: https://github.com/google/adk-python/blob/main/LICENSE
- type: DomainSecurity
  url: security/agent-skills-domain-security.yml
- type: GitHubOrganization
  url: https://github.com/api-evangelist
- type: JSONSchema
  url: https://raw.githubusercontent.com/api-evangelist/agent-skills/refs/heads/main/json-schema/agent-skills-tool-schema.json
  title: Tool Schema
- type: JSONSchema
  url: https://raw.githubusercontent.com/api-evangelist/agent-skills/refs/heads/main/json-schema/agent-skills-tool-call-schema.json
  title: Tool Call Schema
- type: JSONSchema
  url: https://raw.githubusercontent.com/api-evangelist/agent-skills/refs/heads/main/json-schema/agent-skills-tool-result-schema.json
  title: Tool Result Schema
- type: JSONSchema
  url: https://raw.githubusercontent.com/api-evangelist/agent-skills/refs/heads/main/json-schema/agent-skills-mcp-server-schema.json
  title: MCP Server Schema
- type: JSONLD
  url: https://raw.githubusercontent.com/api-evangelist/agent-skills/refs/heads/main/json-ld/agent-skills-context.jsonld
- type: Vocabulary
  url: https://raw.githubusercontent.com/api-evangelist/agent-skills/refs/heads/main/vocabulary/agent-skills-vocabulary.yaml
- type: Features
  data:
  - name: Function Calling
    description: AI agents can invoke user-defined or platform-provided functions based on natural language instructions,
      with structured input/output schemas.
  - name: Server-Side Tool Execution
    description: Platforms like Anthropic and OpenAI run certain agent skills (web search, code execution) on their own infrastructure,
      removing the need for client-side execution.
  - name: MCP Integration
    description: The Model Context Protocol provides a universal adapter layer enabling agents to discover and call any MCP-compatible
      server as a skill.
  - name: Multi-Agent Orchestration
    description: Frameworks like Google ADK support coordinating multiple specialized agents, with skills delegated across
      agent boundaries via protocols like A2A.
  - name: Strict Schema Enforcement
    description: Agent skill definitions can enforce strict JSON Schema compliance to ensure agents produce well-formed tool
      calls matching the declared parameter schema.
  - name: Tool Discovery
    description: Anthropic's tool_search server tool enables agents to discover available tools at runtime without statically
      declaring all tool schemas upfront.
- type: UseCases
  data:
  - name: Automated Research
    description: Agents use web search and fetch skills to retrieve, synthesize, and summarize information from the internet
      in response to user queries.
  - name: Code Generation and Execution
    description: Agents invoke code execution skills to write, run, and debug code within sandboxed environments, returning
      results to the user.
  - name: Data Integration
    description: Agents use OpenAPI-backed skills to read and write data across enterprise systems — CRMs, ERPs, databases
      — through standardized API calls.
  - name: File and Document Management
    description: Agents invoke file system skills to read, write, and organize documents, images, and structured data on behalf
      of users.
  - name: Multi-Step Workflow Automation
    description: Agents chain multiple skills in sequence — searching, retrieving, transforming, and storing data — to complete
      complex multi-step tasks autonomously.
  - name: AI-Assisted Customer Support
    description: Customer service agents use CRM lookup, ticketing, and knowledge base skills to resolve customer issues without
      human escalation.
- type: Integrations
  data:
  - name: Claude (Anthropic)
    description: Native support for tool use and MCP via the Anthropic Messages API.
  - name: ChatGPT (OpenAI)
    description: Function calling and MCP tool integration via the OpenAI Responses API.
  - name: Gemini (Google)
    description: Tool use and ADK integration for Gemini-based agents.
  - name: VS Code Copilot
    description: GitHub Copilot supports MCP servers as agent skill providers within the VS Code development environment.
  - name: Cursor
    description: Cursor IDE supports MCP tool integration for AI-assisted coding agents.
  - name: LangChain
    description: Open-source framework for composing agent skills into chains and graphs across multiple LLM providers.
  - name: LlamaIndex
    description: Data framework enabling agents to index and retrieve from external data sources as structured skills.
maintainers:
- FN: Kin Lane
  email: info@apievangelist.com

Agent Skills API — frequently asked questions

Does Agent Skills have an API?

Yes. APIs.io lists 3 APIs from Agent Skills, including Anthropic Tool Use API, Google Agent Development Kit (ADK) and Model Context Protocol (MCP).

How much does the Agent Skills API cost?

APIs.io lists 3 pricing plans for Agent Skills, with each plan's price, limits and inclusions under Pricing on this page.

Is the Agent Skills API ready for AI agents?

Agent Skills has a Kin Score of 28.2 out of 100 (thin) and an Agent Readiness score of 0.6 (human-only), scored by API Evangelist on 2026-11-06.

Work with this as data

Every provider here is available over the APIs.io API and to AI agents over MCP.

MCP server

One button, every client — Claude, Cursor, VS Code and the rest.

https://apis.io/mcp

Tools for providers

9 MCP tools reach this

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apis_io_searchSTART HERE — APIs, providers and tags for one query, each with its total.
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