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Verdict MCP

by Dgotliebversion 0.1.0a4i★ –iRuns on your computer
Registry nameio.github.Dgotlieb/verdict-mcp

Install

Overview

Description from the maker (original English)

Sandboxed test runs for coding agents: compact typed verdicts with failure fingerprints and history

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Developer tools

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • You fill in yourself: VERDICT_PROJECT

You need

  • PyPIPython’s uv installed (for the uvx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "verdict": {
      "command": "uvx",
      "args": [
        "verdict-mcp"
      ],
      "env": {
        "VERDICT_PROJECT": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add verdict --env VERDICT_PROJECT=your-value -- uvx verdict-mcp

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "verdict": {
      "command": "uvx",
      "args": [
        "verdict-mcp"
      ],
      "env": {
        "VERDICT_PROJECT": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "verdict": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "verdict-mcp"
      ],
      "env": {
        "VERDICT_PROJECT": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

  • VERDICT_PROJECT env
    Path to the project to verify (defaults to cwd)

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account Dgotlieb, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Developer toolsi

  • More stars than 0% of the other 4908 servers in this category
  • Published to the registry more recently than 37% of the other 4908 servers in this category
  • Basic checks equal to or better than 100% of the other 4908 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Verdict MCP safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.1.0a4) reached the registry 2 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: GitHub, Desktop Commander, Cloudflare MCP

Alternatives

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

verdict

https://github.com/user-attachments/assets/62923912-98aa-4246-8bc8-4adf55ae3ff8

Structured, sandboxed verification feedback for coding agents.

An MCP server that replaces your agent's pytest shell-outs with something built for the agent inner loop: impact-selected tests, run in an isolated environment, returning compact typed verdicts instead of 40,000 tokens of raw runner output — with failure fingerprints that tell the agent whether a failure is *its* regression or was broken all along.

raw pytest dump:  ~40,000 tokens, unstructured, run un-sandboxed on your machine

verdict: ~400 tokens, typed JSON, run in a rootless container, with memory

Why

The highest-frequency tool call in agentic coding is verification — and it's the least structured. Agents re-run whole suites when one module changed, burn context parsing ANSI-coded tracebacks, run arbitrary code directly on your machine, and routinely misdiagnose pre-existing breakage as their own regression (then "fix" code that wasn't broken). verdict fixes all four.

Tools

ToolWhat it does
verify(scope?, base?)Selects tests affected by your working-tree diff (static import graph via grimp), runs them via podman/docker with the worktree mounted read-only, returns typed failures with fingerprints and a preexisting flag
explain_failure(check_id)Full traceback for one failure, on demand — bulk never rides in the summary
history(fingerprint)First seen / last seen / times seen — regression vs. long-standing breakage
run_checks(["ruff","mypy"])Lint and type checks, normalized into the same verdict schema

Every failure carries a fingerprint: a stable hash of the normalized failure signature (volatile tokens — addresses, tmp paths, ids, durations — collapsed). Same logical failure, same fingerprint, across runs and refactors. Fingerprints are what give verdict memory.

Quickstart

No install step needed — uvx fetches it on first use. (Or uv tool install verdict-mcp / pip install verdict-mcp for a permanent verdict-mcp command.)

Claude Code — .mcp.json in your project root:

{

"mcpServers": { "verdict": { "command": "uvx", "args": ["verdict-mcp"], "env": { "VERDICT_PROJECT": "." } } } }

Cursor — same shape in .cursor/mcp.json.

Optional verdict.toml in your repo root:

[project]

packages = ["your_package"] # for impact selection (auto-guessed if omitted)

[runner] image = "ghcr.io/you/yourproj-test" # prebuilt env with your deps setup_cmd = "pip install -e .[test]" # or install on the fly (runs with network; tests don't)

prefer = "local" # escape hatch if you have no container runtime

[limits] max_failures = 10

Try it without an agent:

cd examples/demo_project

VERDICT_PROJECT=. verdict-mcp # then connect any MCP client, or use the MCP inspector

Sandbox posture (v0.1)

Checks run in an ephemeral container (podman preferred, docker fallback): worktree mounted read-only at /src, copied to a writable /work inside the container, --network=none for the check run. Your host environment is never mutated by a test run. If setup_cmd is configured, that step runs *with* network before the check; prefer a prebuilt image for a tighter posture. No container runtime → explicit prefer = "local" fallback runs checks against a temp copy of your worktree (still never in place). See SECURITY.md for the full threat model and known limitations.

Troubleshooting: if a verdict says container engine 'podman' could not start the check, run the suggested podman pull by hand — the engine's own error is the answer. One known trap on macOS: a "credsStore": "gcloud" line in ~/.docker/config.json makes podman call the gcloud credential helper for *every* registry, including docker.io; an expired gcloud login then breaks all pulls. Fix with gcloud auth login or remove that line.

Honest limitations

  • Impact selection uses the static import graph — approximate by design. Dynamic imports, fixture-by-name resolution, and data-driven tests can be missed; verify(scope="all") is always available and verdict says in selection_note whenever it falls back.
  • Python/pytest only today, plus ruff/mypy. The adapter interface is small and documented — vitest and go test -json adapters are the most-wanted contributions (CONTRIBUTING.md).
  • Flake detection and coverage-map-based selection are v0.2 (roadmap).

Roadmap

v0.2: coverage-based impact maps (precise selection), flake detection via fingerprint alternation, devcontainer.json support, result cache keyed on (tree hash, check, image digest). Later: vitest/jest, go test, cargo test adapters; per-repo verdict daemon mode.

License

Apache-2.0

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