A
licenseNot graded
qualityC
maintenanceProtects AI agents from bad tool outputs, schema mismatches, and tool poisoning by validating, sanitizing, and scoring reliability of tool responses.
MIT
Agent Output Guard MCP
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TDQS
B3.4/5.0
Scored across 5 tools
Disambiguation3/5
Most tools have distinct purposes—schema validation, hallucination detection, freshness checks, and consistency scoring all target different concerns. However, cross_reference_check and output_consistency_score overlap somewhat in that both produce consistency/reliability assessments, and an agent could struggle to choose between these two when the objective is 'check consistency.'
Naming Consistency3/5
The naming pattern is mostly consistent, using descriptive verb_noun compounds (verify_json_schema, detect_hallucination_markers, cross_reference_check). However, there's inconsistency in the verb forms: 'verify,' 'detect,' 'validate,' 'cross_reference' (noun-ified verb), and 'output' (pure noun). The naming style is readable but not uniformly patterned.
Tool Count5/5
Five tools is well-scoped for an output-guard server. Each tool addresses a distinct quality dimension (schema, hallucination, freshness, cross-source consistency, overall score), and the count feels right without redundancy or unnecessary proliferation.
Completeness3/5
The server covers the core guard concerns—schema, hallucination, freshness, and consistency—but it lacks some common guard functions such as an actual sanitation/repair tool to fix or block invalid output, or a tool to enforce output length/format limits. There's no explicit quarantine or rejection workflow creating a dead end where issues are detected but not actionable.