Iris — The Agent Eval Standard for MCP

Know whether your AI agents are actually good enough to ship. Iris is an open-source MCP server that scores output quality, catches safety failures, and enforces cost budgets 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. Infrastructure 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 evaluates all of it.

What You Get

Requires Node.js 20 or later. Check with node --version.

MCP Tools

Iris registers nine tools that any MCP-compatible agent can invoke — full rule + trace lifecycle + LLM-as-judge + semantic citation verification:

• 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 (heuristic, deterministic, free)
• get_traces — Query stored traces with filtering, pagination, and time-range support
• list_rules — Enumerate deployed custom eval rules (read-only)
• deploy_rule — Register a new custom eval rule so it fires on every evaluate_output of that category
• delete_rule — Remove a deployed custom rule (destructive, idempotent)
• delete_trace — Remove a single stored trace by ID (destructive, tenant-scoped)
• evaluate_with_llm_judge — Semantic eval via LLM (Anthropic or OpenAI). Five templates: accuracy, helpfulness, safety, correctness, faithfulness. Cost-capped, per-eval pricing disclosed. Bring your own API key (IRIS_ANTHROPIC_API_KEY or IRIS_OPENAI_API_KEY) — Iris doesn't proxy or relay LLM calls.
• verify_citations — Extract citations from output (numbered, author-year, URLs, DOIs), fetch sources behind an SSRF-guarded + domain-allowlisted resolver, and use an LLM judge to check whether each source actually supports the cited claim. Opt-in outbound HTTP. Same BYOK requirement as evaluate_with_llm_judge.

When IRIS_OTEL_ENDPOINT is configured, log_trace calls also emit a best-effort OTLP/HTTP JSON export to any OpenTelemetry collector (Jaeger, Grafana Tempo, Datadog OTLP, Honeycomb, etc). See docs/otel-integration.md.

Full tool schemas and configuration: iris-eval.com

Cloud Tier (Coming Soon)

Self-hosted Iris runs on your machine with SQLite. As your team's eval needs grow, the cloud tier adds PostgreSQL, team dashboards, alerting on quality regressions, 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 — connect and invoke tools
• HTTP transport (TS + Python) — full client code for REST-style integration
• LangChain instrumentation (Python, conceptual) — scaffold showing the shape; needs your agent code to be runnable
• CrewAI instrumentation (Python, conceptual) — scaffold; same caveat

Quickstart

Add Iris to your MCP config. Works with Claude Desktop, Claude Code, Cursor, Windsurf, VS Code, Cline, Zed, Codex CLI, Gemini CLI — and any other MCP-compatible agent.

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

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

Turn on the dashboard

Iris ships with a real-time web dashboard showing traces, eval results, cost breakdowns, and rule pass-rates. It's off by default so the MCP server stays lightweight — flip it on with a flag.

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

Then open http://localhost:6920 after your agent runs a trace. The same dashboard is available via CLI:

npx @iris-eval/mcp-server --dashboard

claude mcp add --transport stdio iris-eval -- npx @iris-eval/mcp-server

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

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

[mcp_servers.iris-eval]
command = "npx"
args = ["@iris-eval/mcp-server"]

Other Install Methods

# Global install (recommended for persistent data and faster startup)
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

Tip: Global install (npm install -g) stores traces persistently at ~/.iris/iris.db. With npx, traces persist in the same location, but startup is slower due to package resolution.