Quality Transformation Coach Assistent MCP server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| GITHUB_TOKEN | Yes | Your GitHub Personal Access Token for accessing issue/PR data |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
| logging | {} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| analyze_code_qualityB | Scan a repository for code quality metrics (complexity, duplication, smells). |
| analyze_test_coverageB | Analyze test coverage by comparing test files against source files. |
| detect_test_patternsC | Detect test patterns and anti-patterns in a repository. |
| analyze_flaky_testsC | Detect flaky tests from CI run data. |
| defect_trend_analysisB | Analyze issue trends (volume, resolution time, severity) for a repository. |
| quality_hotspot_detectionB | Identify files with the highest defect density (hotspots). |
| root_cause_categoriesB | Categorise closed bugs by root cause (code, design, requirements, security). |
| pipeline_healthA | Analyze CI/CD pipeline health (success rate, duration trends). |
| test_result_summaryA | Get test results summary for a specific CI workflow run. |
| quality_gate_checkC | Check if quality gates are met for a project. |
| maturity_assessmentC | Run a TMMi maturity assessment for a project. |
| quality_recommendationsC | Generate prioritised quality improvement recommendations. |
| framework_lookupA | Look up information about a quality framework (ISO 25010, TMMi). |
| generate_quality_reportC | Generate a comprehensive Markdown quality report. |
| cicd_readiness_scanB | Run a CI/CD Readiness Scan assessment with 102 questions across 5 domains. |
| ai_readiness_scanB | Run an AI Readiness Scan assessment with 60 questions across 5 AI domains. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 16 tools
Each tool targets a distinct aspect of quality (pipeline health, test results, code quality, readiness scans, defects), and the descriptions help differentiate them. There is mild overlap between analysis tools like analyze_code_quality and quality_hotspot_detection, but their scopes are clear enough to avoid major misselection.
All names are snake_case and descriptive, making the set readable. However, the convention is mixed: some tools use noun phrases (pipeline_health, framework_lookup) while others use verb-led names (analyze_code_quality, detect_test_patterns, generate_quality_report), so the naming pattern is not fully consistent.
At 16 tools, the set is slightly over the ideal 3-15 range but still reasonable given the broad quality transformation domain. Each tool earns its place by covering a distinct part of the assessment, analysis, and reporting workflow.
The tool surface covers assessment, analysis, recommendations, and report generation across code quality, testing, CI/CD, and maturity frameworks. A minor gap is the lack of operational follow-up tools such as tracking remediation progress, but the core workflow is well covered.