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 publishes 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.
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read its repository directly, so Open Source Surface carries
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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 90 points between them,
because the conditional facet above carries the other
10. That is why each facet's contribution is shown against a damped
maximum: raising a quality facet moves the composite by 90% of its nominal
weight, not 100%. The full arithmetic is at apis.io/rating/.
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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'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...
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...
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.
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: medium
source:
- plans
generated: '2026-07-22'
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: '2026-04-19'
specificationVersion: '0.23'
tags:
- 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
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: 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
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