google-a2a
About Pure java implementation of Google A2A protocol. This project demonstrates how to build an A2A-compliant multi-agent system using Spring Boot, Ollama, and the Agent-to-Agent (A2A) protocol.
Install / Use
claude mcp add kuldeepsingh99 -- npx -y github:kuldeepsingh99/google-a2aIf the server publishes to npm under a different name, use that package instead — check the repo README.
MCP Server
Model Context Protocol server
Quality Score
Category
Development & EngineeringSupported Platforms
Tags
Our assessment of google-a2a
google-a2a scores 53/100 on our quality scale, 4232nd of 4,611 Development & Engineering skills we index.
Its MCP Server is 2.4 KB long, split into 5 sections and no code examples: moderately detailed.
It has 3 GitHub stars, so there is little community track record yet; judge it on its content.
Maintenance, license and trust
- The repository was last updated about 14 months ago. Expect some instructions to reference tool versions or APIs that have since changed.
- Our last check on 2026-09-20 found the source still online.
- No license is declared. By default that means all rights are reserved: you can read it, but reusing or redistributing it is not clearly permitted. Ask the author before building on it commercially.
- Its trust signals score 68/100, with 3 cautions from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
Safety scan
No issues foundOur scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful.
AI review by kimi-k2.7-code on 2026-09-29. Automated pattern scan on 2026-09-29. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.
google-a2a compared with similar skills
All 4 of these similar skills score higher than google-a2a; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| google-a2a (this skill)by kuldeepsingh99 | 53 | 3 | 14mo ago | MCP Server |
| taste-skillby Leonxlnx | 100 | 110.6k | 1d ago | CLAUDE.md |
| Agent-Reachby Panniantong | 100 | 109.2k | today | CLAUDE.md |
| headroomby headroomlabs-ai | 100 | 78.9k | today | CLAUDE.md |
| ai-job-searchby ai-job-search-dev | 100 | 69.8k | today | CLAUDE.md |
Frequently asked questions
- How do I install google-a2a?
- Run
claude mcp add kuldeepsingh99 -- npx -y github:kuldeepsingh99/google-a2a. The install tabs above show the steps for each supported agent. - Which AI agents does google-a2a work with?
- It is written for Claude Code and Claude Desktop, as a MCP Server file. Other agents that read the same format can often use it too.
- Is google-a2a safe to use?
- Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. An AI review of the same text found nothing harmful. It declares no license and scores 68/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is google-a2a still maintained?
- The repository was last updated about 14 months ago. Expect some instructions to reference tool versions or APIs that have since changed.
Skill content
View source on GitHubJava Implementation of Google's A2A Protocol (A2A-Compliant)
This project demonstrates how to build an A2A-compliant multi-agent system using Spring Boot, Ollama, and the Agent-to-Agent (A2A) protocol.
The architecture is based on a Parent Agent that orchestrates multiple Child Agents (e.g., Order Agent, Seller Agent). The parent dynamically discovers child agents via their AgentCard (agent.json) and delegates tasks in a standardized, message-driven way.
Key Features
A2A-Compliant Communication – Agents communicate through the A2A protocol using standardized Message objects and AgentCard descriptors.
Dynamic Agent Discovery – The parent automatically fetches AgentCards from configured child agents, ensuring no hardcoding of capabilities.
LLM-Driven Task Routing – The parent uses a Large Language Model (via Ollama) to decide which agent(s) should handle a given user query.
Chained Orchestration – The parent can call multiple agents in sequence, e.g., fetching order details from the Order Agent and then using the seller ID to fetch additional info from the Seller Agent.
Response Aggregation – The parent aggregates responses from multiple agents into a final, natural-language answer for the user.
Strict Agent Boundaries – Each agent is independent and exposes its own agent.json, making the system modular and extendable.
Use Cases
Case 1: Get B2C Orders → Only the Order Agent is called.
Case 2: Get Seller Info by ID → Only the Seller Agent is called.
Case 3: Get B2C Orders with Seller Info → The Order Agent is called first, then the Seller Agent is called using the extracted seller ID(s).
Flow Diagram
<img width="1100" height="688" alt="MCP SERVER" src="https://github.com/user-attachments/assets/5d9587bc-1b59-46f6-94a4-cc5655eb101f" /><img width="942" height="1144" alt="a2a drawio" src="https://github.com/user-attachments/assets/9074448a-2991-4a02-82d3-aef06f25d257" />
Testing with Prompts
<img width="1360" height="741" alt="Screenshot 2025-09-03 225131" src="https://github.com/user-attachments/assets/27ed83ae-04eb-4140-ab16-b4859711da63" /><img width="1360" height="614" alt="Screenshot 2025-09-03 224131" src="https://github.com/user-attachments/assets/3c0be40e-8a66-4c15-aa2b-5d6e72ea721f" />
<img width="1368" height="879" alt="Screenshot 2025-09-03 221828" src="https://github.com/user-attachments/assets/4e6e96f1-be99-4f25-ac99-cdca8f2f32fd" />
Related Skills
taste-skill
110.6kAgent skills that give coding agents taste: art direction, reference images and a browser check instead of generic AI pages
Agent-Reach
109.2kGive your AI agent eyes to see the entire internet. Read & search Twitter, Reddit, YouTube, GitHub, Bilibili, XiaoHongShu — one CLI, zero API fees.
headroom
78.9kCompress tool outputs, logs, files, and RAG chunks before they reach the LLM. 20% fewer tokens for coding agents, 60-95% fewer tokens for JSON, same answers. Library, proxy, MCP server.
ai-job-search
69.8kThe job search that runs on your machine. AI job application framework built on Claude Code: evaluate postings, tailor CVs, write cover letters, prep interviews. Fork it and own it.
Languages
Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.