llm-output-quality-monitor
Cloudflare Workers MCP server: llm-output-quality-monitor
Sollte ich dies verwenden
Qualität und Sicherheit
Befunde (1)
- LOWTool 'hallucination_scorer' description lacks action verbin hallucination_scorer
Basierend auf einer automatisierten Analyse der Tool-Definitionen und der Einhaltung des Protokolls.
Kontextkosten
Dies ist die ungefähre Anzahl der Tokens, die jedes Mal verbraucht werden, wenn die Tools des Servers in den Kontext eines Modells geladen werden. Höhere Werte verringern die Aufmerksamkeit, die für andere Aufgaben verfügbar ist.
Installieren
Installation mit einem Klick
Fügen Sie dies Ihrer Datei `claude_desktop_config.json` hinzu:
{
"mcpServers": {
"llm-output-quality-monitor": {
"url": "https://api.lazy-mac.com/llm-output-quality-monitor/mcp"
}
}
}Remote-Endpunkte
https://api.lazy-mac.com/llm-output-quality-monitor/mcpstreamable-httpWas es kann
Tool-Inventar
Tools (5)
⚪quality_validator(response, minLength, maxLength, strictFormat)
Validate LLM response quality based on length, format, and structure
Eingabe-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
Eingabe-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.
Eingabe-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
Eingabe-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
Eingabe-Schema
{
"type": "object",
"properties": {
"responses": {
"type": "array",
"items": {
"type": "string"
},
"description": "Array of responses to compare"
}
},
"required": [
"responses"
]
}Community
Nachweis