Qc MCP Server

MCP server for runtime quality validation of AI agent outputs — hallucination detection, scope compl

Local serverstdio

What is the Qc MCP server?

MCP server for runtime quality validation of AI agent outputs — hallucination detection, scope compl. Exposed over MCP by the qc mcp server, that capability becomes something an assistant can invoke while it works, not something you go and do afterwards.

What it actually does

Runtime quality validation for AI agent outputs. Detect hallucinations, enforce scope compliance, and score output quality — all via MCP.

Its toolset

Everything the assistant can do here goes through one of these:

  • validate_output — Score agent output against configurable criteria: length limits, required keywords, forbidden patterns, and factual claim density
  • check_hallucination_risk — Estimate hallucination likelihood. With source text, checks sentence-level grounding. Without source, flags outputs dense with specific numbers
  • check_scope_compliance — Validate output against a scope contract — allowed/forbidden topics, word limits, required sections
  • log_validation — The log_validation tool exposed by this server
  • get_failure_patterns — The get_failure_patterns tool exposed by this server
  • generate_quality_report — Quality dashboard across all validated agents — no parameters required

Adding it to your client

The server ships on npm as qc-validator-mcp, so your MCP client can launch it on demand — there is no separate build step. Add the server block to your client's configuration, restart it, and the tools register themselves.

When to reach for it

This sits in the developer tooling group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. Qc's toolset — validate_output, check_hallucination_risk, check_scope_compliance and 3 more — is a fair guide to whether it matches your workflow. It is maintained by mdfifty50-boop; worth a glance at recent repository activity before you build anything load-bearing on it.

We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.

Caveats

  • It runs with your machine's permissions. That is convenient and also the reason to think about what you point it at before you approve a tool call.
  • MCP clients confirm each tool call by default. Leave that on until you have watched what the qc mcp server does with a few real requests.

Available tools

ToolWhat it does
validate_outputScore agent output against configurable criteria: length limits, required keywords, forbidden patterns, and factual claim density.
check_hallucination_riskEstimate hallucination likelihood. With source text, checks sentence-level grounding. Without source, flags outputs dense with specific numbers, dates, and URLs.
check_scope_complianceValidate output against a scope contract — allowed/forbidden topics, word limits, required sections.
log_validationThe log_validation tool exposed by this server.
get_failure_patternsThe get_failure_patterns tool exposed by this server.
generate_quality_reportQuality dashboard across all validated agents — no parameters required.

How to install the Qc MCP server

### Claude Desktop

```json
{
  "mcpServers": {
    "qc-validator": {
      "command": "npx",
      "args": ["qc-validator-mcp"]
    }
  }
}

Configuration as documented by the project. Restart the client after saving.

Example prompts to try

  • Use Qc to validate output.
  • Use Qc to check hallucination risk.
  • Use Qc to check scope compliance.

Frequently asked questions

It connects Qc to MCP-compatible AI assistants such as Claude and Cursor, exposing 6 tools (validate_output, check_hallucination_risk, check_scope_compliance, and more) that the assistant can call on your behalf. Instead of copying data back and forth by hand, the assistant works with Qc directly.