qc-validator-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
| resources | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| validate_outputA | Score agent output quality against configurable criteria. Checks length, required keywords, forbidden patterns, claim density, and task relevance. |
| check_hallucination_riskA | Estimate hallucination likelihood in agent output. If source text is provided, checks grounding. Otherwise flags outputs with high counts of specific numbers, dates, and URLs. |
| check_scope_complianceB | Validate that agent output stays within a defined scope contract. Checks allowed/forbidden topics, word limits, and required sections. |
| log_validationA | Store a validation result for trending and failure pattern analysis. Accumulates per-agent statistics over time. |
| get_failure_patternsA | Analyze common failure modes for a specific agent. Returns pass rate, average score, most frequent issue types, and quality trend direction. |
| generate_quality_reportA | Generate a quality dashboard for all validated agents. Shows per-agent summaries, overall pass rate, worst/best performers, and actionable recommendations. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| dashboard |
TDQS
Scored across 6 tools
The tools are mostly distinct: validate_output provides general scoring, while check_hallucination_risk and check_scope_compliance target specific failure modes. There is slight overlap between validate_output's forbidden patterns and check_scope_compliance's allowed/forbidden topics, but the descriptions clarify different scopes.
All tool names follow a consistent verb_noun pattern (validate_output, get_failure_patterns, check_hallucination_risk, check_scope_compliance, log_validation, generate_quality_report), using lowercase with underscores throughout. This makes the API predictable and easy to navigate.
With 6 tools, the server is well-scoped for its purpose of validating agent output. Each tool covers a distinct step in the validation workflow: executing checks, analyzing patterns, logging results, and reporting, without unnecessary bloat.
The core workflow (validate, log, analyze, report) is covered. However, there is no tool to manage the configurable criteria mentioned in validate_output, which is a minor gap. Additionally, update/delete operations for logged results are missing, but the main lifecycle is intact.