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Lujo-MCP MCP server · F · ToolBench

Lujo-MCP

MCP Runtime Debugging Context Server for AI coding agents. Gives Claude, Cursor, Trae and any MCP-compatible client real runtime debugging context: browser errors, console logs, network failures, UI interaction traces and session context.

<=2025-11-25STDIO14 tools

38/100

Definition Quality26

Protocol Readiness47

Supportability52

Spec posture

Inferred effective spec <=2025-11-25·current baseline 2026-07-28

Static source inference · medium confidence · detected: stateful session

Deprecated protocol patterns detected

Summary

Critical failure across all 14 tools. Descriptions exist but are generic (20-80 chars), lacking parameter details, return types, and LLM-actionable guidance. No parameter descriptions visible. No output schemas documented. Tools like 'blame' and 'analyze_with_llm' are vague about what they return or require. No error handling guidance. This server is not production-ready for agent use.

Tools (14)

analyze_with_llmread onlyauth33/100

Analyze runtime debugging context using LLM to identify root causes

blameread onlyauth23/100

Identify which component or code change caused a failure

diagnose_issueread onlyauth33/100

Diagnose issues based on collected runtime data and traces

get_debug_contextread onlyauth38/100

Get comprehensive runtime debugging context including console logs, network failures, and session state

get_runtime_snapshotread onlyauth38/100

Capture a snapshot of current runtime state including memory, CPU, and active processes

get_stacktraceread onlyauth37/100

Retrieve stack trace information from runtime debugging context

ingest_errorwriteauth33/100

Ingest error reports from browser SDK or Node.js SDK

ingest_log

Issues (5)

CRITICALconstrained-input ↗

ALL 14 tools have empty input schemas ({}). LLMs cannot infer parameter types, constraints, or required fields.

get_stacktraceget_debug_contextget_runtime_snapshotsearch_logslist_recent_tracesverifyverify_uianalyze_with_llmingest_erroringest_networkingest_logingest_ui_eventdiagnose_issueblame

CRITICALtool-description ↗

No parameter descriptions visible in any tool. Descriptions like 'Verify runtime state against expected specifications' do not explain what parameters the tool accepts, their types, or constraints.

verifyverify_uisearch_logsanalyze_with_llm

CRITICALresponse-shaper ↗

No output schemas documented. Tools like 'diagnose_issue' and 'analyze_with_llm' do not specify what fields they return, forcing LLMs to guess structure and preventing downstream tool chaining.

Recommendations

Add explicit JSON Schema to every tool's input. Example for 'search_logs': {"type": "object", "properties": {"query": {"type": "string", "description": "Search term or regex pattern"}, "limit": {"type": "integer", "minimum": 1, "maximum": 100, "default": 20}}, "required": ["query"]}
Expand tool descriptions to 100-200 chars and include WHEN to use the tool. Example: 'search_logs: Search collected runtime logs by keyword or regex. Use this after get_debug_context to find specific error patterns or network failures. Returns matching log entries with timestamps.'
Document output schemas for all tools. Example for 'diagnose_issue': Returns {"found": boolean, "root_cause": string, "affected_components": [string], "suggested_fix": string}
Rename ambiguous tools: 'verify' → 'verify_runtime_state', 'verify_ui' → 'verify_ui_against_spec', 'blame' → 'identify_failing_component'
Add error handling guidance to ingest_* tools: 'Returns {"error": "Invalid payload format"} if SDK data is malformed. Retry with corrected payload. If error persists, check SDK version compatibility.'

Spec posture evidence

Inferred effective spec: <=2025-11-25.

Relies on Stateful initialize / Mcp-Session-Id (removed; protocol is stateless) - make each request self-contained

Score history

Overall score trend

First recorded score · v2 rubric

38/100

Scored	Grade	Overall	Spec posture	Rubric
2026-09-23	F	38	<=2025-11-25	v2