MCP server

@izri/mcp exposes Izri's signals as Model Context Protocol tools. The point is closing the loop: the agent that wrote the diff can ask whether the diff is safe to merge, and act on the answer, without a human relaying the verdict.

Setup

The server needs the same two environment variables as the CLI: IZRI_API_URL and IZRI_API_TOKEN.

Claude Code

claude mcp add izri \
  --env IZRI_API_URL=https://api.izri.ai \
  --env IZRI_API_TOKEN=izri_xxxxxxxxxxxx \
  -- npx -y @izri/mcp

Cursor / Windsurf

Add to your MCP configuration file:

{
  "mcpServers": {
    "izri": {
      "command": "npx",
      "args": ["-y", "@izri/mcp"],
      "env": {
        "IZRI_API_URL": "https://api.izri.ai",
        "IZRI_API_TOKEN": "izri_xxxxxxxxxxxx"
      }
    }
  }
}

The nine tools

Discovery

list_projects — lists the projects your token can reach. Read-only and safe to call any time.

Call this first. An API token only sees projects in its own organization, and skipping discovery is how agents land on a confusing cross-organization NOT_FOUND.

get_scope_config — reads the project's scope config: the team vocabulary the analyzer reasons against (modules, categories, sensitive paths), plus its version and source (derived, user, or merged). Free on every tier. Returns null when nothing has been derived yet.

The headline verdict

score_pr — the answer to "is this delta safe to merge?" in one read. Returns the izri/quality umbrella verdict (passing / failing / unknown), per-signal child contexts, aggregate scores, hard findings, and LLM insights.

This is the tool an agent should reach for by default. The rest are drill-downs.

Running tests

run_suite — triggers a test run and returns a test_run_id immediately. The run is asynchronous; poll get_results.

get_results — fetches a run's current state by ID: status (PENDING / RUNNING / PASSED / FAILED / ERROR), counts, duration, and logs once complete. Pass enriched=true for cached LLM insights — Hobby tier or higher.

Signal drill-downs

check_pr_scope — compares a PR's declared scope (title, labels, body) against the actual diff and flags drift. Returns the deterministic rule layer by default; semantic: true adds the LLM intent comparison and costs AI tokens.

check_hallucinations — the per-file diff-coverage breakdown for a delta: coverage_score 0–100, file buckets (covered / partial / uncovered / not-instrumented), rule findings, and per-file covered and uncovered lines.

check_test_efficacy — the same shape as check_hallucinations, but addressed by test_run_id instead of by delta. Use it right after run_suite when you want the verdict for that exact attempt rather than the latest one on the delta.

get_suggestions — aggregates recurring themes, likely causes, and suspected flakes across the project's recent reports. Answers "what's the pattern in our recent PRs?" Hobby tier or higher; free-tier callers get PAYMENT_REQUIRED.

A working loop

The pattern worth teaching an agent:

1. list_projects once, to get a valid project ID.
2. Write the change, open the PR.
3. score_pr — read the umbrella verdict.
4. If failing, drill in: check_pr_scope for scope drift, check_hallucinations for untested changes.
5. Fix, push, score_pr again.

Note step 5. Izri never proposes the patch; it reports what is wrong and the agent decides what to do about it.

Tier gating

list_projects, get_scope_config, score_pr, run_suite, get_results, check_pr_scope, check_hallucinations, and check_test_efficacy work on every plan. get_suggestions and enriched results need Hobby or higher. See plans and quotas.

Related

• CLI — the same signals from a terminal.
• The four signals — what each tool is actually measuring.