Chiark MCP Server

MCP server for AI agent discovery and quality scoring. Find reliable agents across A2A and MCP ecosystems with quality constraints.

Powered by chiark.ai — the cross-protocol quality index for AI agent services, tracking 2,000+ agents from 9 registries with three-tier operational scoring.

Quick Start

Use the hosted endpoint (recommended)

Add to your MCP client config (Claude Code, Cursor, etc.):

{
  "mcpServers": {
    "chiark": {
      "url": "https://chiark.ai/mcp/"
    }
  }
}

Install locally

pip install chiark-mcp

Add to your MCP client config:

{
  "mcpServers": {
    "chiark": {
      "command": "chiark-mcp"
    }
  }
}

Or run directly:

python -m chiark_mcp

Related MCP server: AgentRank

Tools

find_agent

Search for AI agents by task description with quality constraints.

find_agent(
  task_description="web scraping",
  min_uptime=0.95,
  max_latency_ms=500,
  protocol="mcp",
  max_results=5
)

Returns ranked agents with scores, uptime, latency, endpoint URLs.

check_agent_status

Check if an agent is alive right now.

check_agent_status(agent_id="uuid-from-find-results")

Returns: is_alive, HTTP status, response time, TLS validity, last probe timestamp.

get_agent_score

Get full quality score breakdown.

get_agent_score(agent_id="uuid")

Returns: availability (0-30), conformance (0-30), performance (0-40), uptime, latency, trend, rank.

report_outcome

Report whether a routed agent succeeded or failed. Improves future recommendations.

report_outcome(agent_id="uuid", success=true, task_category="translation")

get_ecosystem_stats

Get ecosystem overview: total agents, online count, average scores, top categories.

get_ecosystem_stats()

How It Works

Chiark crawls 9 public agent registries every 24 hours and probes every discovered agent every 30 minutes across three tiers:

1. Availability — Is it alive? HTTP status, response time, TLS

2. Conformance — Does it follow its declared protocol correctly?

3. Performance — How fast does it respond? Task completion rate

Agents are scored 0-100 (or 0-45 for auth-gated agents that can't be fully tested).

Constraint Filters

Links

• Site: https://chiark.ai

• API docs: https://chiark.ai/docs

• Hosted MCP endpoint: https://chiark.ai/mcp/

• llms.txt: https://chiark.ai/llms.txt

• Agent Card: https://chiark.ai/.well-known/agent.json

License

MIT