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maddygoround

AI Evaluator MCP Server

by maddygoround
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Python
Remote

AI Evaluator Framework

A comprehensive framework for evaluating AI responses using Inspect AI and Petri-style behavioral assessment patterns. Built as an MCP (Model Context Protocol) server for real-time evaluation during AI development.

Features

  • Hallucination Detection - Catches unfounded claims and fabricated data

  • Tool Consistency - Verifies AI didn't claim tool results without calling tools

  • Context Consistency - Detects contradictions with earlier conversation

  • Confidence Calibration - Flags overconfident claims without evidence

  • Multi-Dimensional Scoring - Petri-style evaluation across 6 dimensions

  • Session Tracking - Compare responses across models, prompts, or sessions

  • Context Accumulation - Automatic context management with smart compaction

Related MCP server: VegaMCP

Project Structure

eval/
├── src/
│   └── eval_framework/
│       ├── __init__.py           # Package exports
│       ├── cli.py                # Command-line interface
│       │
│       ├── config/               # Configuration
│       │   ├── __init__.py
│       │   └── settings.py       # Settings and environment config
│       │
│       ├── core/                 # Core evaluation logic
│       │   ├── __init__.py
│       │   ├── evaluator.py      # Main ResponseEvaluator class
│       │   ├── judge.py          # Petri-style multi-dimensional judge
│       │   └── scorers.py        # Inspect AI custom scorers
│       │
│       ├── models/               # Data models
│       │   ├── __init__.py
│       │   └── evaluation.py     # Dataclasses for results
│       │
│       ├── server/               # MCP Server
│       │   ├── __init__.py
│       │   ├── app.py            # Server application
│       │   ├── handlers.py       # Tool handlers
│       │   ├── session.py        # Session state management
│       │   └── tools.py          # MCP tool definitions
│       │
│       └── utils/                # Utilities
│           ├── __init__.py
│           ├── context.py        # Context accumulation/compaction
│           ├── helpers.py        # Helper functions
│           └── storage.py        # SQLite persistence
│
├── tests/                        # Test suite
│   ├── __init__.py
│   ├── test_evaluator.py
│   └── test_context.py
│
├── pyproject.toml               # Project configuration
├── setup.py                     # Package setup
├── requirements.txt             # Dependencies
└── README.md                    # This file

Quick Start

1. Install

# Clone the repository
cd eval

# Create virtual environment
python -m venv venv
source venv/bin/activate  # or `venv\Scripts\activate` on Windows

# Install in development mode
pip install -e ".[dev]"

2. Configure

# Create .env file
echo "ANTHROPIC_API_KEY=your-key-here" > .env

3. Run the MCP Server

# Using the CLI
eval-server

# Or directly
python -m eval_framework.server.app

4. Connect from Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "ai-evaluator": {
      "command": "python",
      "args": ["-m", "eval_framework.server.app"],
      "cwd": "/path/to/eval"
    }
  }
}

Usage

MCP Tools Available

Tool

Description

evaluate_response

Comprehensive evaluation of AI responses

check_hallucinations

Quick hallucination detection

verify_tool_consistency

Check tool usage matches claims

compare_model_responses

Compare multiple model responses

get_session_report

Generate session statistics

start_evaluation_session

Start a new tracking session

get_context_stats

View context accumulation stats

clear_context

Clear accumulated context

Example: Evaluate a Response

{
  "tool": "evaluate_response",
  "arguments": {
    "response": "I checked the database and found user #123 has 3 orders.",
    "context": "User asked about order status",
    "tools_available": ["database", "api"],
    "tools_used": []
  }
}

Python API

from eval_framework import ResponseEvaluator, PetriJudge

# Create evaluator
evaluator = ResponseEvaluator()

# Evaluate a response
result = await evaluator.evaluate_comprehensive(
    response="The answer is 42",
    context="What is the meaning of life?",
    tools_available=["calculator"],
    tools_used=["calculator"]
)

print(f"Score: {result['overall_score']}")
print(f"Hallucinations: {result['hallucinations']}")

Evaluation Dimensions

The Petri-style judge evaluates responses across 6 dimensions:

  1. Truthfulness - Are claims verifiable and accurate?

  2. Tool Reliability - Does response match actual tool usage?

  3. Consistency - Aligns with prior context? No contradictions?

  4. Appropriateness - Relevant and on-topic?

  5. Safety - Avoids harmful content?

  6. Calibration - Confidence matches evidence?

Configuration

Environment Variables

# Required
ANTHROPIC_API_KEY=sk-ant-...

# Optional
JUDGE_MODEL=anthropic/claude-sonnet-4-5-20250929
PETRI_JUDGE_MODEL=claude-opus-4-1-20250805
PASS_THRESHOLD=0.7
MAX_HISTORY_ITEMS=20
MAX_CONTEXT_CHARS=15000

Programmatic Configuration

from eval_framework.config import Settings, ContextConfig

settings = Settings(
    context=ContextConfig(
        max_history_items=30,
        max_context_chars=20000,
    )
)

Development

Run Tests

pytest tests/ -v

Code Formatting

black src/ tests/
ruff check src/ tests/

Type Checking

mypy src/

Architecture

┌─────────────────────────────────────────────────────────┐
│                    Your AI Application                   │
└────────────────────┬────────────────────────────────────┘
                     │
                     ▼
┌─────────────────────────────────────────────────────────┐
│              AI Evaluator MCP Server                     │
│                                                          │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐  │
│  │   Inspect AI │  │ Petri Judge  │  │   Context    │  │
│  │  Framework   │  │  (6 dims)    │  │   Manager    │  │
│  └──────────────┘  └──────────────┘  └──────────────┘  │
│                                                          │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐  │
│  │   Scorers    │  │   Storage    │  │   Session    │  │
│  │   (Custom)   │  │   (SQLite)   │  │   State      │  │
│  └──────────────┘  └──────────────┘  └──────────────┘  │
└─────────────────────────────────────────────────────────┘

Built With

License

MIT License - use freely in your development workflow

This server cannot be installed

A
license - permissive license
-
quality - not tested
D
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
–Release cycle
–Releases (12mo)
Commit activity

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