A2A Protocol Skill#

An AI agent skill for building, connecting, and operating A2A-compliant agents. This skill is the definitive guide for agents participating in the Agent2Agent protocol ecosystem.

Core Position#

This skill is NOT a general API wrapper — it is a protocol education and implementation guide. It teaches agents what A2A is, how to structure AgentCards for discovery, which operations to perform in which order, and how to handle the multi-turn task lifecycle.

This skill is NOT framework-specific. It guides agents regardless of whether they are built with LangGraph, BeeAI, Google ADK, or custom code. The A2A protocol is language-agnostic.

This skill covers the JSON-RPC 2.0 over HTTP binding (the most common) plus key concepts for gRPC and HTTP+REST bindings.

What Problem This Solves#

AI agents today operate in silos. Even when they share the same infrastructure, they have no standard way to:

• Discover what capabilities other agents expose
• Send a task to a remote agent and track its lifecycle
• Handle streaming updates or push notifications from a remote agent
• Exchange files, structured data, or multi-part messages

The A2A protocol solves all of the above. This skill makes an AI agent A2A-aware.

Modes#

/a2a-protocol#

Default mode. Learn A2A concepts, data structures, and operation semantics.

Preferred opening: "The A2A protocol enables agents to discover each other and collaborate without exposing internal state. Here is the protocol overview:"

Use it when:

• The user asks what A2A is, how it works, or why it matters
• You need to explain the difference between A2A and MCP
• You want to understand the AgentCard structure and discovery mechanism
• You need to understand the full task lifecycle (submitted → working → completed)

/a2a-protocol build-card#

Generate an AgentCard JSON for the agent. The AgentCard is the public identity document that other agents use to discover capabilities, auth requirements, and connection endpoints.

Preferred opening: "To publish this agent in the A2A ecosystem, I will generate an AgentCard at /.well-known/agent.json declaring: name, version, capabilities, and auth requirements."

Use it when:

• You are building an A2A server agent and need to publish its capabilities
• You need a starting-point AgentCard template to customize
• The user wants to register an agent in the A2A ecosystem

Output: Writes agent-card.json to the workspace with the complete AgentCard.

/a2a-protocol send-task#

Send a task to a remote A2A agent via tasks/send. Includes building the request, interpreting the response (synchronous or task ID for async), and handling errors.

Preferred opening: "To delegate this task to the remote agent, I will: (1) fetch its AgentCard, (2) validate capability compatibility, (3) construct a SendMessageRequest and POST it."

Use it when:

• You need to delegate a subtask to another agent
• You are acting as an A2A client initiating work on a remote agent
• The user says "send this task to the X agent" or "delegate to agent Y"

Output: Resolves to artifacts[].parts from the completed Task.

/a2a-protocol stream-task#

Set up streaming task delivery via tasks/sendSubscribe with SSE (Server-Sent Events), receive incremental TaskStatusUpdateEvent and TaskArtifactUpdateEvent blocks, and reassemble the final artifact.

Preferred opening: "This remote task will take time. I will subscribe via SSE to receive real-time status transitions and partial artifacts as they arrive."

Use it when:

• A remote agent's task will take time and you want real-time progress updates
• You need to stream partial results as they become available
• The user says "track the task progress" or "watch for updates"

Output: Reassembles the final Artifact from accumulated streaming events.

/a2a-protocol subscribe-task#

Subscribe to a task's push notification channel via tasks/subscribe, using either SSE streaming or webhook delivery to receive final results.

Preferred opening: "I will register a push notification subscription so the remote agent delivers the result to our callback endpoint rather than requiring us to poll."

Use it when:

• You want the remote agent to push results back to you rather than polling
• You need asynchronous result delivery to a callback URL
• The user says "notify me when done" or "push results to this endpoint"

Output: Receives and validates the push payload; extracts taskId + artifacts.

/a2a-protocol mock-server#

Start a local mock A2A server for experimentation. The mock server is a fully functional A2A agent that serves its own AgentCard, accepts tasks via all supported operations, and responds with realistic simulated results.

Preferred opening: "To experiment with A2A locally, I will start a mock A2A server on a free port. This gives us a real A2A endpoint to practice against."

Use it when:

• You want to test A2A interactions without a real remote agent
• You need a safe sandbox to practice send-task, stream-task, or subscribe-task
• You want to verify that the agent correctly implements the A2A protocol before deploying
• The user says "try it out", "test A2A", "run locally", or "experiment"

Output: Starts an HTTP server at http://localhost:<port>/a2a/ with a ready AgentCard.

This mode is a no-op for agents without Python execution capability. If the host cannot run Python scripts, skip this mode.

Execution Steps#

Mode: /a2a-protocol (Learn)#

1. You read the A2A spec overview from references/a2a-overview.md
2. You review the data model objects from references/data-model.md
3. You answer the user's question using the reference material
4. If the user asks for a specific operation, you switch to the appropriate mode

Mode: /a2a-protocol build-card#

1. You read references/agent-card-template.md for the full AgentCard schema
2. You determine which capabilities the agent exposes (text, forms, media, streaming)
3. You determine auth scheme (none, API key, OAuth2, mTLS)
4. You determine push notification support (SSE, webhook, both, neither)
5. You generate a completed AgentCard JSON and write it to agent-card.json
6. You confirm the agent card URL path (default: /.well-known/agent.json)

Mode: /a2a-protocol send-task#

1. You read references/operations.md — SendMessage operation details
2. You identify the target agent's URL (from its AgentCard or given address)
3. You check the target agent's auth requirements (from its AgentCard)
4. You construct a SendMessageRequest with taskId, sessionId, message, and context
5. You send POST JSON-RPC 2.0 request to the target agent's A2A endpoint
6. You handle the response: immediate result (synchronous) or taskId for async polling
7. If async: you use tasks/get to poll for completion or tasks/sendSubscribe for streaming
8. On completion: you extract artifacts[].parts from the task result and return them

Mode: /a2a-protocol stream-task#

1. You read references/operations.md — SendStreamingMessage operation + streaming events
2. You construct a SendMessageRequest and set stream: true in configuration
3. You send tasks/sendSubscribe POST to the target agent's A2A endpoint
4. You receive SSE stream of TaskStatusUpdateEvent (status transitions) and TaskArtifactUpdateEvent (partial artifact updates)
5. You accumulate events until TaskStatusUpdateEvent with state: completed/ failed/ canceled
6. You close the SSE stream and extract the final artifact from accumulated data

Mode: /a2a-protocol subscribe-task#

1. You read references/operations.md — SubscribeToTask operation + push notification setup
2. You determine delivery mode: SSE stream (server-push via tasks/sendSubscribe) or webhook push (agent registers a callback URL via tasks/sendSubscribe with pushConfig)
3. For webhook mode: you register PushNotificationConfig with the target's tasks/sendSubscribe including pushConfig.url and pushConfig.authentication
4. You wait for delivery; validate the incoming tasks/push payload using the AuthenticationInfo
5. You extract taskId and artifacts from the validated push payload and return them

Mode: /a2a-protocol mock-server#

1. You locate scripts/mock_a2a_server.py in the skill directory
2. You start the server: python3 scripts/mock_a2a_server.py --port <PORT> --name <NAME> --skills <SKILLS>
3. You fetch the AgentCard from http://localhost:<PORT>/.well-known/agent.json to confirm it started
4. You optionally run scripts/validate_agent_card.py against the fetched AgentCard
5. You use send-task, stream-task, or subscribe-task against the local endpoint to test interactions
6. When done: you stop the server (Ctrl+C or SIGTERM)

Mandatory Rules#

Do not#

• Do not hardcode any AgentCard endpoint URLs — always use the supplied or discovered URL
• Do not assume unauthenticated access — always check the target agent's AgentCard.authentication
• Do not send a task without a sessionId if the operation requires multi-turn context
• Do not discard TaskStatusUpdateEvent blocks — they signal working/progress states
• Do not treat A2A as a replacement for MCP — A2A is for agent-to-agent collaboration, MCP is for agent-to-tool/resource connectivity; use both together
• Do not expose the agent's internal memory, tool implementations, or proprietary state in the AgentCard or in task payloads — A2A preserves agent opacity
• Do not return the raw task object — always extract and return artifacts[].parts
• Do not close an SSE stream before receiving final: true on a TaskStatusUpdateEvent

Do#

• Always validate capability compatibility before sending a task (check targetAgent.capabilities.streaming and pushNotifications)
• Always handle streaming with proper event accumulation — never treat partial events as final
• Always close SSE streams when the task reaches a terminal state
• Always use idempotent task IDs when retrying a failed tasks/send
• Always include context.contextId to thread multi-turn conversations across agents
• Always return structured output ( artifacts[].parts) not raw JSON-RPC responses

Quality Bar#

A good A2A interaction must satisfy all of:

• AgentCard fetched from /.well-known/agent.json (HTTP 200, valid JSON, has required fields)
• JSON-RPC 2.0 request has jsonrpc: "2.0", method, params.taskId (or server-generated), and id
• Task state machine transitions are followed: no completed before a working signal received
• Streaming: TaskStatusUpdateEvent.final: true received before closing stream
• Artifact extraction: parts[].text or parts[].data returned, not raw task object
• Auth: correct scheme used (Bearer token → Authorization: Bearer, API key → X-API-Key, etc.)
• Error handling: JSON-RPC error codes -32700/ -32600/ -32603 recognized and surfaced as errors

A bad A2A interaction exhibits any of:

• No capability check before sending (assumes all agents accept all tasks)
• SSE stream closed before receiving final: true on the terminal status event
• Raw task object returned instead of extracted artifacts[].parts
• Wrong HTTP method used (GET instead of POST, or wrong path for the operation)
• Auth credentials omitted when AgentCard.authentication.credentials: "required"

Good vs. Bad Examples#

Good: Sending a task with full capability check#

text

1. Fetch AgentCard from https://target-agent/.well-known/agent.json
2. Verify capabilities.streaming or dataStreamingSupported matches our needs
3. Check authentication.schemes and prepare credentials if credentials: "required"
4. Construct SendMessageRequest: taskId=uuid(), sessionId, message with parts
5. POST to https://target-agent/a2a/tasks/send with Authorization header
6. Receive { task: { taskId: "abc123", status: { state: "submitted" } } }
7. Poll tasks/get until status.state === "completed"
8. Return artifacts[0].parts[0].text  ← structured output

Bad: Sending a task without discovery#

text

1. POST to https://target-agent/a2a/tasks/send with hardcoded payload
2. Assume success without checking status.state
3. Return raw task object instead of artifacts[].parts

Good: AgentCard with complete capability declaration#

json

{
  "name": "data-analysis-agent",
  "description": "Specialized in financial data extraction and reporting",
  "version": "1.0",
  "capabilities": {
    "streaming": "SUPPORTED",
    "pushNotifications": "SUPPORTED",
    "agentPrivilege": false,
    "signedAgentCard": false
  },
  "skills": [
    { "id": "financial-data", "name": "Financial Data Extraction", "description": "..." }
  ],
  "authentication": {
    "schemes": ["httpBearer"],
    "credentials": "required"
  }
}

Bad: Generic AgentCard without capability detail#

json

{
  "name": "agent",
  "description": "An AI agent"
}

References#

• references/a2a-overview.md — A2A protocol overview, key goals, A2A vs MCP comparison
• references/data-model.md — Core data model (Task, Message, Part, Artifact, AgentCard, streaming events)
• references/operations.md — All A2A operations reference (send, get, subscribe, push, cancel, list)
• references/agent-card-template.md — AgentCard JSON schema with full annotated example
• references/quickstart.md — Full end-to-end example: AgentCard → discovery → send → streaming → result
• scripts/mock_a2a_server.py — Start a local mock A2A agent for experimentation and testing