verdict
Runs pytest tests in an isolated container, with impact-based test selection and returning compact typed verdicts that include failure fingerprints and pre-existing failure flags.
Runs ruff lint checks as part of the verification workflow, normalizing results into the same verdict schema used for test failures.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@verdictverify tests affected by my latest changes"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
verdict
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 memoryWhy
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.
Related MCP server: Debugging MCP Server
Tools
Tool | What it does |
| 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 |
| Full traceback for one failure, on demand — bulk never rides in the summary |
| First seen / last seen / times seen — regression vs. long-standing breakage |
| 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 = 10Try it without an agent:
cd examples/demo_project
VERDICT_PROJECT=. verdict-mcp # then connect any MCP client, or use the MCP inspectorSandbox 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 <image> 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 inselection_notewhenever it falls back.Python/pytest only today, plus ruff/mypy. The adapter interface is small and documented — vitest and
go test -jsonadapters 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
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Servers
- AlicenseAqualityDmaintenanceProvides secure access to containerized build environments for software projects, enabling AI assistants to execute builds, run tests, manage git operations, and inspect build artifacts without requiring local installation of dependencies.6MIT
- FlicenseAqualityDmaintenanceEnables LLMs to automatically diagnose coding errors through codebase search, test execution, and live debugger integration (DAP/V8 CDP). Provides a secure, policy-gated environment for investigating failures while preventing destructive operations.9
- FlicenseNot gradedqualityDmaintenanceProvides AI coding agents with dependency analysis, impact detection, and build verification tools.
- AlicenseAqualityCmaintenanceProvides an isolated workspace for testing candidate code, runs tests, and returns deterministic pass/fail verdicts. Enables automated grading of software engineering solutions by ensuring reproducible test runs.5MIT
Related MCP Connectors
Deterministic context layer for your codebase: change impact, blast radius, answers with receipts.
Deterministic pre-execution audit for trading agents. PASS/WAIT/FAIL, reproducible verdict_hash.
Proves AI-generated Python does what you asked: lint, types, security, sandbox run, exact fixes.
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/Dgotlieb/verdict-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server