Output quality control and validation for AI agents
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.
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-mdfifty50-boop-qc-validator": {
"args": [
"-y",
"qc-validator-mcp"
],
"command": "npx"
}
}
}From the project's GitHub README.
Runtime quality validation for AI agent outputs. Detect hallucinations, enforce scope compliance, and score output quality — all via MCP.
npx qc-validator-mcp
{
"mcpServers": {
"qc-validator": {
"command": "npx",
"args": ["qc-validator-mcp"]
}
}
}
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 }
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 }
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 }
Store validation results for per-agent trending.
Params: agent_id, output_hash, score, pass, issues_count
Returns: { logged, agent_id, total_validations }
Analyze common failure modes for a specific agent.
Params: agent_id
Returns: { total_validations, pass_rate, avg_score, most_common_issues[], trend }
Quality dashboard across all validated agents — no parameters required.
Returns: { total_agents, overall_pass_rate, agents[], worst_performers[], best_performers[], recommendations[] }
qc://dashboard — Quality metrics for all validated agentsMIT
Be the first to review this server!
by Modelcontextprotocol · Developer Tools
Read, search, and manipulate Git repositories programmatically
by Modelcontextprotocol · Developer Tools
Web content fetching and conversion for efficient LLM usage
by Toleno · Developer Tools
Toleno Network MCP Server — Manage your Toleno mining account with Claude AI using natural language.