About
Output quality control and validation for AI agents
Security Report
Valid MCP server (1 strong, 1 medium validity signals). No known CVEs in dependencies. Package registry verified. Imported from the Official MCP Registry.
14 files analyzed · 1 issue found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
How to Install
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-mdfifty50-boop-qc-validator": {
"args": [
"-y",
"qc-validator-mcp"
],
"command": "npx"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
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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