Verdict MCP
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., "@Verdict MCPSubmit auth_handler.py for audit"
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.
The Problem
AI agents suffer from Context Drift, Illusion of Completion, and Quality Degradation:
❌ Mark tasks DONE but leave empty pass statements
❌ Skip critical error handling
❌ Generate unstyled "developer art" UIs
❌ Write superficial tests with no real assertions
❌ No accountability — human must review everything manuallyRelated MCP server: Architect-to-Product (A2P)
The Solution
Verdict is a programmatic gatekeeper that sits between the AI agent and your codebase. Every task must pass all three gates before being marked complete:
Features
Quick Start
1. Install
pip install verdict-mcp
# OR from source:
git clone https://github.com/Zubair-z/VERDICT-MCP.git
cd VERDICT-MCP
pip install -r requirements.txt2. Scaffold a project
verdict init myproject
cd myprojectThis creates: plan.md + auth_handler.py + db_handler.py + tests/test_auth_handler.py
3. Run as MCP Server (for AI agents)
python -m verdict_mcp4. Run as REST API (for any HTTP client)
python -m verdict_mcp.api_server
# → http://localhost:8000
curl http://localhost:8000/health5. Run full validation pipeline
curl -X POST http://localhost:8000/pipeline/run \
-H "Content-Type: application/json" \
-d '{
"task_id": "TASK_001",
"source_files": ["auth_handler.py"],
"test_file": "tests/test_auth_handler.py",
"target_file": "auth_handler.py",
"ui_file": "main_window.py"
}'Architecture
graph LR
A[AI Agent] -->|MCP / HTTP| B{Verdict}
B --> C[AST Auditor]
B --> D[UI Enforcer]
B --> E[Test Runner]
C --> F[pass/TODO/err/docstring checks]
D --> G[glassmorphism/neon/layout]
E --> H[pytest + coverage + mutation]
F & G & H --> I{TASK COMPLETED?}
I -->|Yes| J[Git Snapshot]
I -->|No| AAPI Endpoints
Method | Path | Description |
|
| Server health |
|
| Initialize plan |
|
| Get task states |
|
| Task details |
|
| AST audit files |
|
| Design tokens |
|
| Validate UI |
|
| Coverage + mutation |
|
| Coverage report |
|
| Full lifecycle |
Multi-Language Support
Language | Extensions | Checks Applied |
Python |
| Full AST audit (pass, TODO, try-except, docstrings) |
JavaScript |
| TODO, empty functions, missing try-catch |
TypeScript |
| TODO, empty functions, missing try-catch |
React |
| TODO, empty components, missing error handling |
Go |
| TODO, empty funcs, missing error handling |
Rust |
| TODO, empty fn, missing error handling |
Java |
| TODO, empty methods, missing try-catch |
Kotlin |
| TODO, empty fun, missing error handling |
CLI Commands
# Scaffold a new project
verdict init myproject
# Run Verdict checks on current project
verdict verify
# Start MCP server
python -m verdict_mcp
# Start REST API
python -m verdict_mcp.api_server
# Run tests locally
python -m pytest tests/ -v --cov=verdict_mcp --cov-report=term-missingDocker
# Start REST API
docker-compose up verdict-mcp
# Start MCP stdio mode
docker-compose up verdict-mcp-stdioVS Code Extension
Copy
.vscode-extension/folder to~/.vscode/extensions/verdict-mcp/Restart VS Code
Open a Python file → press
Ctrl+Shift+VOr enable auto-audit on save in settings
Contributing
We welcome contributions! See CONTRIBUTING.md for guidelines.
This server cannot be installed
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
- AlicenseAqualityCmaintenanceA local MCP server that packages LLM evaluation gates as reusable CI/CD primitives, enabling AI agents to run datasets against models, score responses, and enforce quality thresholds.Last updated10MIT
- Alicense-qualityCmaintenanceAn MCP server that adds engineering discipline to AI-assisted development, enforcing evidence-gated TDD, security review, backup strategy, and deployment generation to turn AI-generated code into production-ready software.Last updated1210MIT
- FlicenseAqualityCmaintenanceAn MCP server implementing a 7-stage agentic frontend workflow—from design audit to PR review—including AI-driven component generation, browser validation, E2E testing, and CI self-healing.Last updated48
Related MCP Connectors
A paid remote MCP for AI agent browser approval MCP, built to return verdicts, receipts, usage logs,
A paid remote MCP for AI SDK MCP gateway registry, built to return verdicts, receipts, usage logs, a
A paid remote MCP for CodeG, built to return verdicts, receipts, usage logs, and audit-ready JSON.
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/Zubair-z/VERDICT-MCP'
If you have feedback or need assistance with the MCP directory API, please join our Discord server