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
Featured
Keep your Mac awake while Claude Code and 40+ AI agents run. Sleeps when they're idle.
One time payment $9 →Integrate web data into your AI product. One API to scrape website & brand data.
Get API Key Now →Agent, run crypto. Access onchain data & trade routes via 1inch.
Install now →On Capafy, your Skill runs online 24/7 as an agent product, and you get paid every time someone uses it.
Start earning →