io.github.mdfifty50-boop/qc-validator
Output quality control and validation for AI agents
Open source Open in the app JSON README (API)
About
Output quality control and validation for AI agents
Details
- Kind
- MCP servers
- Topic
- No topic detected
- Publisher
- mdfifty50-boop
- Origin
- official
- Category
- ferramentas
- Transport
- local
- Version
- 0.1.3
- Last push
- 2026-04-24T13:40:59Z
- Repository state
- ativo
- Language
- JavaScript
- Added
- 2026-08-29 04:00:46
- Updated
- 2026-08-29 04:00:46
- Origin id
io.github.mdfifty50-boop/qc-validator
README
# qc-validator-mcp
Runtime quality validation for AI agent outputs. Detect hallucinations, enforce scope compliance, and score output quality — all via MCP.
## Install
```bash
npx qc-validator-mcp
```
### Claude Desktop
```json
{
"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