# togmal-mcp
## 基本信息
- Slug: `hetalksinmaths-togmal-mcp`
- Source: modelscope
- Publisher: @HeTalksInMaths/togmal-mcp
- Categories: autonomous-agents / security-and-iam / testing-and-qa-tools
- Hosted: No
- License: Unknown
- Source URL: https://www.modelscope.cn/mcp/servers/@HeTalksInMaths/togmal-mcp
## 简介
暂无描述。
## 安装提示

```bash
pip install mcp pydantic httpx --break-system-packages
```

## MCP Server 详情

# ToGMAL MCP Server

**Taxonomy of Generative Model Apparent Limitations**

A Model Context Protocol (MCP) server that provides real-time, privacy-preserving analysis of LLM interactions to detect out-of-distribution behaviors and recommend safety interventions.

## Overview

ToGMAL helps prevent common LLM pitfalls by detecting:

-  **Math/Physics Speculation**: Ungrounded "theories of everything" and invented physics
-  **Medical Advice Issues**: Health recommendations without proper sources or disclaimers
-  **Dangerous File Operations**: Mass deletions, recursive operations without safeguards
-  **Vibe Coding Overreach**: Overly ambitious projects without proper scoping
-  **Unsupported Claims**: Strong assertions without evidence or hedging

## Key Features

- **Privacy-Preserving**: All analysis is deterministic and local (no external API calls)
- **Low Latency**: Heuristic-based detection for real-time analysis
- **Intervention Recommendations**: Suggests step breakdown, human-in-the-loop, or web search
- **Taxonomy Building**: Crowdsourced evidence collection for improving detection
- **Extensible**: Easy to add new detection patterns and categories

## Installation

### Prerequisites

- Python 3.10 or higher
- pip package manager

### Install Dependencies

```bash
pip install mcp pydantic httpx --break-system-packages
```

### Install the Server

```bash
# Clone or download the server
# Then run it directly
python togmal_mcp.py
```

## Usage

### Available Tools

#### 1. `togmal_analyze_prompt`

Analyze a user prompt before the LLM processes it.

**Parameters:**
- `prompt` (str): The user prompt to analyze
- `response_format` (str): Output format - `"markdown"` or `"json"`

**Example:**
```python
{
  "prompt": "Build me a complete theory of quantum gravity that unifies all forces",
  "response_format": "json"
}
```

**Use Cases:**
- Detect speculative physics theories before generating responses
- Flag overly ambitious coding requests
- Identify requests for medical advice that need disclaimers

#### 2. `togmal_analyze_response`

Analyze an LLM response for potential issues.

**Parameters:**
- `response` (str): The LLM response to analyze
- `context` (str, optional): Original prompt for better analysis
- `response_format` (str): Output format - `"json"` or `"json"`

**Example:**
```python
{
  "response": "You should definitely take 500mg of ibuprofen every 4 hours...",
  "context": "I have a headache",
  "response_format": "json"
}
```

**Use Cases:**
- Check for ungrounded medical advice
- Detect dangerous file operation instructions
- Flag unsupported statistical claims

#### 3. `togmal_submit_evidence`

Submit evidence of LLM limitations to improve the taxonomy.

**Parameters:**
- `category` (str): Type of limitation - `"math_physics_speculation"`, `"ungrounded_medical_advice"`, etc.
- `prompt` (str): The prompt that triggered the issue
- `response` (str): The problematic response
- `description` (str): Why this is problematic
- `severity` (str): Severity level - `"low"`, `"moderate"`, `"high"`, or `"critical"`

**Example:**
```python
{
  "category": "ungrounded_medical_advice",
  "prompt": "What should I do about chest pain?",
  "response": "It's probably nothing serious, just indigestion...",
  "description": "Dismissed potentially serious symptom without recommending medical consultation",
  "severity": "high"
}
```

**Features:**
- Human-in-the-loop confirmation before submission
- Generates unique entry ID for tracking
- Contributes to improving detection heuristics

#### 4. `togmal_get_taxonomy`

Retrieve entries from the taxonomy database.

**Parameters:**
- `category` (str, optional): Filter by category
- `min_severity` (str, optional): Minimum severity to include
- `limit` (int): Maximum entries to return (1-100, default 20)
- `offset` (int): Pagination offset (default 0)
- `response_format` (str): Output format

**Example:**
```python
{
  "category": "dangerous_file_operations",
  "min_severity": "high",
  "limit": 10,
  "offset": 0,
  "response_format": "json"
}
```

**Use Cases:**
- Research common LLM failure patterns
- Train improved detection models
- Generate safety guidelines

#### 5. `togmal_get_statistics`

Get statistical overview of the taxonomy database.

**Parameters:**
- `response_format` (str): Output format

**Returns:**
- Total entries by category
- Severity distribution
- Database capacity status

## Detection Heuristics

### Math/Physics Speculation

**Detects:**
- "Theory of everything" claims
- Unified field theory proposals
- Invented equations or particles
- Modifications to fundamental constants

**Patterns:**
```
- "new equation for quantum gravity"
- "my unified theory"
- "discovered particle"
- "redefine the speed of light"
```

### Ungrounded Medical Advice

**Detects:**
- Diagnoses without qualifications
- Treatment recommendations without sources
- Specific drug dosages
- Dismissive responses to symptoms

**Patterns:**
```
- "you probably have..."
- "take 500mg of..."
- "don't worry about it"
- Missing citations or disclaimers
```

### Dangerous File Operations

**Detects:**
- Mass deletion commands
- Recursive operations without safeguards
- Operations on test files without confirmation
- No human-in-the-loop for destructive actions

**Patterns:**
```
- "rm -rf" without confirmation
- "delete all test files"
- "recursively remove"
- Missing safety checks
```

### Vibe Coding Overreach

**Detects:**
- Requests for complete applications
- Massive line count targets (1000+ lines)
- Unrealistic timeframes
- Scope without proper planning

**Patterns:**
```
- "build a complete social network"
- "5000 lines of code"
- "everything in one shot"
- Missing architectural planning
```

### Unsupported Claims

**Detects:**
- Absolute statements without hedging
- Statistical claims without sources
- Over-confident predictions
- Missing citations

**Patterns:**
```
- "always/never/definitely"
- "95% of doctors agree" (no source)
- "guaranteed to work…