llm-output-quality-monitor
Cloudflare Workers MCP server: llm-output-quality-monitor
¿Debería usar esto?
Calidad y seguridad
Hallazgos (1)
- LOWTool 'hallucination_scorer' description lacks action verben hallucination_scorer
Basado en el análisis automatizado de las definiciones de herramientas y el cumplimiento del protocolo.
Costo de contexto
Este es el número aproximado de tokens que se consumen cada vez que las herramientas del servidor se cargan en el contexto de un modelo. Los recuentos más altos reducen la atención disponible para otras tareas.
Instalar
Instalación con un clic
Agrega esto a tu archivo `claude_desktop_config.json`:
{
"mcpServers": {
"llm-output-quality-monitor": {
"url": "https://api.lazy-mac.com/llm-output-quality-monitor/mcp"
}
}
}Puntos de conexión remotos
https://api.lazy-mac.com/llm-output-quality-monitor/mcpstreamable-httpQué puede hacer
Inventario de herramientas
Herramientas (5)
⚪quality_validator(response, minLength, maxLength, strictFormat)
Validate LLM response quality based on length, format, and structure
Esquema de entrada
{
"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
Esquema de entrada
{
"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.
Esquema de entrada
{
"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
Esquema de entrada
{
"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
Esquema de entrada
{
"type": "object",
"properties": {
"responses": {
"type": "array",
"items": {
"type": "string"
},
"description": "Array of responses to compare"
}
},
"required": [
"responses"
]
}Comunidad
Evidencia