MCP server

Llm Output Quality Monitor

io.github.lazymac2x/llm-output-quality-monitor

Checked 28 Oct 2026, 09:03 UTC · Connected to it and listed its tools

Cloudflare Workers MCP server: llm-output-quality-monitor

Source

At a glance

Type

MCP server

Publisher

Not verified

Status

Working at last check

Tools

5

Token cost

About 569 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

28 Oct 2026, 09:03 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http llm-output-quality-monitor https://api.lazy-mac.com/llm-output-quality-monitor/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.llm-output-quality-monitor]
url = "https://api.lazy-mac.com/llm-output-quality-monitor/mcp"

Gemini CLI

{"mcpServers":{"llm-output-quality-monitor":{"httpUrl":"https://api.lazy-mac.com/llm-output-quality-monitor/mcp"}}}

Cursor

{"mcpServers":{"llm-output-quality-monitor":{"url":"https://api.lazy-mac.com/llm-output-quality-monitor/mcp"}}}

VS Code

{"servers":{"llm-output-quality-monitor":{"type":"http","url":"https://api.lazy-mac.com/llm-output-quality-monitor/mcp"}}}

Antigravity

agy mcp add llm-output-quality-monitor https://api.lazy-mac.com/llm-output-quality-monitor/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"llm-output-quality-monitor","server_url":"https://api.lazy-mac.com/llm-output-quality-monitor/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"llm-output-quality-monitor","server_url":"https://api.lazy-mac.com/llm-output-quality-monitor/mcp"}

Tools

• consistency_check ~76 tokens
  Check consistency across multiple LLM responses
• drift_detector ~124 tokens
  Detect quality drift between current and previous LLM responses
• hallucination_scorer ~135 tokens
  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.
• quality_validator ~148 tokens
  Validate LLM response quality based on length, format, and structure
• schema_enforcer ~86 tokens
  Validate JSON response against schema

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