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qc-validator-mcp

qc-validator-mcp

by mdfifty50-boop
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
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log_validation

Store validation results to monitor quality trends and detect failure patterns across agents over time.

Instructions

Store a validation result for trending and failure pattern analysis. Accumulates per-agent statistics over time.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "agent_id": "<agent_id>",
      "output_hash": "<output_hash>",
      "score": "<score>",
      "pass": "<pass>",
      "issues_count": "<issues_count>"
    },
    "name": "log_validation"
  }
}

Input Schema

NameRequiredDescriptionDefault
passYesWhether the output passed validation
scoreYesQuality score from 0-100
agent_idYesUnique identifier for the agent whose output was validated
output_hashYesHash or identifier for the specific output that was validated
issues_countYesNumber of issues found

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.3

TDQS

A3.5/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden of behavioral disclosure. It adds minimal context by stating that statistics accumulate over time, which hints at persistence and aggregation, but it fails to disclose whether the operation is idempotent, whether duplicates are allowed, what happens on repeated calls, or any permissions or rate limit implications.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, two sentences long, and front-loaded with the primary action 'Store'. Every sentence contributes meaningful context about the tool's purpose and aggregation behavior, with no filler or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

This is a moderate-complexity storage tool with well-documented parameters but no output schema or annotations. The description conveys the core purpose but omits details such as expected return values, error conditions, or whether the operation is synchronous. Given the availability of sibling tools, more context on when this fits the workflow would improve completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema provides complete descriptions for all five parameters with 100% coverage, so the baseline is 3. The description adds no additional parameter context, but since the schema already documents each field's meaning, no further clarification is needed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool stores a validation result, with the specific purpose of enabling trending and failure pattern analysis. The verb 'store' plus the resource 'validation result' and the context of accumulating per-agent statistics distinguish it from sibling tools like validate_output or get_failure_patterns, which perform validation or retrieval.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for recording validation outcomes to enable later analysis, but it does not explicitly state when to use this tool versus alternatives. No exclusions or conditions are mentioned, and sibling tools like validate_output or get_failure_patterns are not referenced for differentiation.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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