llm-output-quality-monitor
Cloudflare Workers MCP server: llm-output-quality-monitor
Should I use this
Quality & Safety
Findings (1)
- LOWTool 'hallucination_scorer' description lacks action verbin hallucination_scorer
Based on automated analysis of tool definitions and protocol compliance.
Context Cost
This is the approximate number of tokens consumed each time the server's tools are loaded into a model's context. Higher counts reduce the attention available for other tasks.
Install
One-Click Install
Add this to your `claude_desktop_config.json` file:
{
"mcpServers": {
"llm-output-quality-monitor": {
"url": "https://api.lazy-mac.com/llm-output-quality-monitor/mcp"
}
}
}Remote endpoints
https://api.lazy-mac.com/llm-output-quality-monitor/mcpstreamable-httpWhat it can do
Tool inventory
Tools (5)
⚪quality_validator(response, minLength, maxLength, strictFormat)
Validate LLM response quality based on length, format, and structure
Input Schema
{
"type": "object",
"properties": {
"response": {
"type": "string",
"description": "LLM response to validate"
},
"minLength": {
"type": "number",
"description": "Minimum response length (default: 10)"
},
"maxLength": {
"type": "number",
"description": "Maximum response length (default: 10000)"
},
"strictFormat": {
"type": "boolean",
"description": "Enforce punctuation and capitalization"
}
},
"required": [
"response"
]
}⚪drift_detector(currentResponse, previousResponse, threshold)
Detect quality drift between current and previous LLM responses
Input Schema
{
"type": "object",
"properties": {
"currentResponse": {
"type": "string",
"description": "Current LLM response"
},
"previousResponse": {
"type": "string",
"description": "Previous LLM response"
},
"threshold": {
"type": "number",
"description": "Drift threshold (0-1, default: 0.15)"
}
},
"required": [
"currentResponse",
"previousResponse"
]
}⚪hallucination_scorer(response, context)
Pattern-based heuristic risk scoring for LLM responses (0-100). Detects linguistic signals such as contradictory assertions, unsourced claims, and uncertainty markers. Not a semantic hallucination detector.
Input Schema
{
"type": "object",
"properties": {
"response": {
"type": "string",
"description": "LLM response to analyze"
},
"context": {
"type": "string",
"description": "Reference context for grounding"
}
},
"required": [
"response"
]
}⚪schema_enforcer(response, schema)
Validate JSON response against schema
Input Schema
{
"type": "object",
"properties": {
"response": {
"type": "string",
"description": "JSON response to validate"
},
"schema": {
"type": "object",
"description": "JSON schema definition"
}
},
"required": [
"response",
"schema"
]
}🟢consistency_check(responses)
Check consistency across multiple LLM responses
Input Schema
{
"type": "object",
"properties": {
"responses": {
"type": "array",
"items": {
"type": "string"
},
"description": "Array of responses to compare"
}
},
"required": [
"responses"
]
}Community
Evidence