About chiark-mcp
chiark-mcp is an MCP server in the Agents category: find the most reliable AI agent for any task. Search 2,000+ agents across A2A and MCP with quality filters - min uptime, max latency, score thresholds. Check if an agent is alive before routing to it. Like Artificial Analysis, but for agent services.
Install
git clone https://github.com/chiark-ai-dev/chiark-mcpThis server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.
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README
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
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:
- Availability — Is it alive? HTTP status, response time, TLS
- Conformance — Does it follow its declared protocol correctly?
- 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
| Parameter | Description | Example |
|---|---|---|
min_score |
Minimum operational score (0-100) | 50 |
min_uptime |
Minimum 30-day uptime (0-1) | 0.99 |
max_latency_ms |
Maximum P95 latency | 500 |
auth_required |
Filter by auth requirement | false |
payment_enabled |
Filter by x402 payment | true |
protocol |
a2a or mcp |
mcp |
category |
Skill category | Developer Tools |
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
README copy captured when this server was indexed; it may lag the repository. The original is authoritative.