Iris — MCP-Native Agent Eval & Observability

See what your AI agents are actually doing. Iris is an open-source MCP server that logs every trace, evaluates output quality, and tracks costs across all your agents. Any MCP-compatible agent discovers and uses it automatically — no SDK, no code changes.

The Problem

Your agents are running in production. Traditional monitoring sees 200 OK and moves on. It has no idea the agent just:

• Leaked a social security number in its response
• Hallucinated an answer with zero factual grounding
• Burned $0.47 on a single query — 4.7x your budget threshold
• Made 6 tool calls when 2 would have sufficed

Iris sees all of it.

What You Get

Trace Logging	Hierarchical span trees with per-tool-call latency, token usage, and cost in USD. Stored in SQLite, queryable instantly.
Output Evaluation	12 built-in rules across 4 categories: completeness, relevance, safety, cost. PII detection, prompt injection patterns, hallucination markers. Add custom rules with Zod schemas.
Cost Visibility	Aggregate cost across all agents over any time window. Set budget thresholds. Get flagged when agents overspend.
Web Dashboard	Real-time dark-mode UI with trace visualization, eval results, and cost breakdowns.

Quickstart

Add Iris to your Claude Desktop (or Cursor, Claude Code, Windsurf) MCP config:

{
  "mcpServers": {
    "iris-eval": {
      "command": "npx",
      "args": ["@iris-eval/mcp-server"]
    }
  }
}

That's it. Your agent discovers Iris and starts logging traces automatically.

Want the dashboard?

npx @iris-eval/mcp-server --dashboard
# Open http://localhost:6920

Other Install Methods

# Global install
npm install -g @iris-eval/mcp-server
iris-mcp --dashboard

# Docker
docker run -p 3000:3000 -v iris-data:/data ghcr.io/iris-eval/mcp-server

MCP Tools

Iris registers three tools that any MCP-compatible agent can invoke:

• log_trace — Log an agent execution with spans, tool calls, token usage, and cost
• evaluate_output — Score output quality against completeness, relevance, safety, and cost rules
• get_traces — Query stored traces with filtering, pagination, and time-range support

Full tool schemas and configuration: iris-eval.com

Cloud Tier (Coming Soon)

Self-hosted Iris runs on your machine with SQLite. As your team grows, the cloud tier adds PostgreSQL, team dashboards, alerting, and managed infrastructure.

Join the waitlist to get early access.

Examples

• Claude Desktop setup — MCP config for stdio and HTTP modes
• TypeScript — MCP SDK client usage
• LangChain — Agent instrumentation
• CrewAI — Crew observability

Community

• GitHub Issues — Bug reports and feature requests
• GitHub Discussions — Questions and ideas
• Contributing Guide — How to contribute
• Roadmap — What's coming next

<details> <summary><strong>Configuration & Security</strong></summary>

CLI Arguments

Flag	Default	Description
--transport	stdio	Transport type: stdio or http
--port	3000	HTTP transport port
--db-path	~/.iris/iris.db	SQLite database path
--config	~/.iris/config.json	Config file path
--api-key	—	API key for HTTP authentication
--dashboard	false	Enable web dashboard
--dashboard-port	6920	Dashboard port

Environment Variables

Variable	Description
IRIS_TRANSPORT	Transport type
IRIS_PORT	HTTP port
IRIS_DB_PATH	Database path
IRIS_LOG_LEVEL	Log level: debug, info, warn, error
IRIS_DASHBOARD	Enable dashboard (true/false)
IRIS_API_KEY	API key for HTTP authentication
IRIS_ALLOWED_ORIGINS	Comma-separated allowed CORS origins

Security

When using HTTP transport, Iris includes:

• API key authentication with timing-safe comparison
• CORS restricted to localhost by default
• Rate limiting (100 req/min API, 20 req/min MCP)
• Helmet security headers
• Zod input validation on all routes
• ReDoS-safe regex for custom eval rules
• 1MB request body limits

# Production deployment
iris-mcp --transport http --port 3000 --api-key "$(openssl rand -hex 32)" --dashboard

</details>

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If Iris is useful to you, consider starring the repo — it helps others find it.

MIT Licensed.