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qc-validator-mcp

qc-validator-mcp

by mdfifty50-boop
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qc-validator-mcp

Runtime quality validation for AI agent outputs. Detect hallucinations, enforce scope compliance, and score output quality — all via MCP.

Install

npx qc-validator-mcp

Claude Desktop

{
  "mcpServers": {
    "qc-validator": {
      "command": "npx",
      "args": ["qc-validator-mcp"]
    }
  }
}

Related MCP server: repo-seatbelt

Tools

validate_output

Score agent output against configurable criteria: length limits, required keywords, forbidden patterns, and factual claim density.

Params: output, task_description, criteria { max_length, required_keywords[], forbidden_patterns[], factual_claims_count }
Returns: { pass, score, issues[], recommendation }

check_hallucination_risk

Estimate hallucination likelihood. With source text, checks sentence-level grounding. Without source, flags outputs dense with specific numbers, dates, and URLs.

Params: output, source_text (optional), claim_count (default 5)
Returns: { risk_level, unsupported_claims[], confidence, suggestion }

check_scope_compliance

Validate output against a scope contract — allowed/forbidden topics, word limits, required sections.

Params: output, scope { allowed_topics[], forbidden_topics[], max_words, required_sections[] }
Returns: { compliant, violations[], scope_utilization_percent }

log_validation

Store validation results for per-agent trending.

Params: agent_id, output_hash, score, pass, issues_count
Returns: { logged, agent_id, total_validations }

get_failure_patterns

Analyze common failure modes for a specific agent.

Params: agent_id
Returns: { total_validations, pass_rate, avg_score, most_common_issues[], trend }

generate_quality_report

Quality dashboard across all validated agents — no parameters required.

Returns: { total_agents, overall_pass_rate, agents[], worst_performers[], best_performers[], recommendations[] }

Resource

  • qc://dashboard — Quality metrics for all validated agents

Architecture

  • Pure Node.js ES modules

  • In-memory Maps (no external dependencies)

  • stdio transport via @modelcontextprotocol/sdk

  • Zero configuration required

License

MIT

Available Tools

6 tools
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.

Inputs: output (required), claim_count, source_text.

check_scope_complianceB

Validate that agent output stays within a defined scope contract. Checks allowed/forbidden topics, word limits, and required sections.

Inputs: scope (required), output (required).

generate_quality_reportA

Generate a quality dashboard for all validated agents. Shows per-agent summaries, overall pass rate, worst/best performers, and actionable recommendations.

Inputs: no named parameters.

get_failure_patternsA

Analyze common failure modes for a specific agent. Returns pass rate, average score, most frequent issue types, and quality trend direction.

Inputs: agent_id (required).

log_validationA

Store a validation result for trending and failure pattern analysis. Accumulates per-agent statistics over time.

Inputs: pass (required), score (required), agent_id (required), output_hash (required), +1 more.

validate_outputA

Score agent output quality against configurable criteria. Checks length, required keywords, forbidden patterns, claim density, and task relevance.

Inputs: output (required), criteria (required), task_description (required).

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A3.9/5.0

    Scored across 6 tools

    Disambiguation4/5

    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.

    Naming Consistency5/5

    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.

    Tool Count5/5

    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.

    Completeness4/5

    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.

    Maintenance

    ActivityInactive
    ResponsivenessNo recent community issues

    Resources

    Related MCP Connectors

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