Skip to main content
Glama
Cerios-TechLab

Quality Transformation Coach Assistent MCP server

Quality Gate Check

quality_gate_check

Check if a project meets quality gate criteria using GitHub reference, gate ID, and optional metrics. Spot unmet conditions early to prevent failed releases and support CI/CD decisions.

Instructions

Check if quality gates are met for a project.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
gate_idNoQuality gate to check (default: 'pull_request')pull_request
metricsNoOptional dict of metric values to check against gate criteria
github_refYesGitHub 'owner/repo' reference

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

Annotations are absent, so the description carries the full burden of behavioral disclosure. It only says 'Check if quality gates are met' and does not state whether the operation is read-only, whether it triggers pipeline checks, how metrics are used in the evaluation, or what side effects might occur.

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

Conciseness4/5

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

The description is a single sentence with no wasted words and front-loads the action. However, it borders on restating the tool name and sacrifices useful context for brevity, so it is conciseness without substantial added value.

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

Completeness2/5

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

For a tool with no annotations and several closely related siblings, one vague sentence is not enough. The description does not explain how metrics interact with gate criteria, when the default pull_request gate applies, or how this check differs from code quality analysis. The output schema may cover return shape but not usage context.

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?

Schema description coverage is 100%, and each parameter already has a meaningful description and default value, so the baseline is 3. The prose description adds no additional semantic detail about gate_id, metrics, or github_ref beyond what the schema already provides.

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

Purpose4/5

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

The description clearly uses a verb ('Check') and a resource ('quality gates') to state the tool's core function. However, it does not distinguish this from closely related siblings like analyze_code_quality or quality_recommendations, and 'quality gates' is left somewhat abstract.

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

Usage Guidelines2/5

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

There is no guidance about when to use this tool versus analyze_code_quality, quality_hotspot_detection, or other siblings. The intended usage must be inferred from the tool name and schema; no explicit when-to-use or when-not-to-use context is provided.

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