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Qc Validator

mdfifty50-boop/qc-validator-mcp
STDIOregistry active
Summary

A validation layer that sits between your AI agent and its output. Exposes six tools: validate_output scores responses against length limits and keyword requirements, check_hallucination_risk flags unsupported claims by checking sentence grounding against source text, and check_scope_compliance enforces topic boundaries and required sections. The last three tools (log_validation, get_failure_patterns, generate_quality_report) track validation history per agent so you can spot recurring failure modes. Everything runs in memory with no external dependencies. Reach for this when you need programmatic guardrails on agent responses, especially if you're building multi-agent systems where output quality varies and you want telemetry on which agents drift off scope or hallucinate most often.

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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"]
    }
  }
}

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

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Registryactive
Packageqc-validator-mcp
TransportSTDIO
UpdatedApr 24, 2026
View on GitHub

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