A
licenseNot graded
qualityB
maintenanceMCP server that analyzes AI agent execution logs to calculate reliability scores, detect failure patterns, and suggest concrete improvements for making AI agents more reliable.
MIT
mcp-ai-accountability
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TDQS
A3.6/5.0
Scored across 8 tools
Disambiguation5/5
Each tool targets a distinct activity: session lifecycle (start/end), logging interactions, score retrieval, failure analysis, improvement recommendations, audit reports, and external health checks. No two tools overlap in a way that would cause confusion for an agent.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern in snake_case: start_session, end_session, record_interaction, get_reliability_score, analyze_failures, recommend_improvements, generate_audit_report, check_mcp_health. The naming is predictable and uniform.
Tool Count5/5
8 tools is well-scoped for an accountability/reliability tracking server. Each tool covers a distinct part of the workflow (session management, interaction logging, scoring, analysis, recommendations, reporting, health checks) without redundancy or bloat.
Completeness4/5
The core lifecycle is covered: session start/end, interaction logging, reliability scoring, failure analysis, recommendations, and audit reports. Minor gaps exist such as session history or detailed interaction listing, but the existing tools form a complete workflow for the stated purpose.
Maintenance
ActivitySlowing
ResponsivenessNo issues