Which integrations are available for this server?

Integrates with GitHub for version control, issue tracking, and pull request management, linking defects and test cases to code changes. Integrates with Jenkins to trigger and monitor CI/CD pipelines, enabling automated test execution and reporting. Integrates with JIRA for comprehensive defect and issue tracking, enabling seamless synchronization of bugs, tasks, and test plans.

How do I use qa-mcp-server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@qa-mcp-server Report a critical bug found during login testing" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

qa-mcp-server

by Suleidis9510

Overview Schema Related Servers Score Discussions

JavaScript

Local

🚀 Enterprise-Grade QA MCP Server

Una implementación profesional del Model Context Protocol (MCP) para Quality Assurance, integrando las mejores prácticas de testing, reportería y gestión de defectos.

📋 Tabla de Contenidos

Related MCP server: Software Planning Tool

✨ Características

1. Gestión de Casos de Prueba 📝

Crear, actualizar y eliminar test cases
Organización por prioridad, etiquetas y estado
Trazabilidad con requisitos
Estimación de duración de pruebas

2. Ejecución de Tests 🧪

Registro detallado de resultados
Tracking por environment
Captura de errores y stack traces
Métricas de tiempo de ejecución
Estadísticas de pass/fail rate

3. Gestión de Defectos 🐛

Reportería integral de bugs
Clasificación por severidad (critical, high, medium, low)
Priorización (P0-P3)
Workflow de estados
Asignación a desarrolladores
Trazabilidad con test cases

4. Análisis de Cobertura 📊

Reporte de cobertura de código por línea
Análisis por archivo
Identificación de áreas no cubiertas
Histórico de tendencias
Visualización de gaps

5. Planificación de Tests 📅

Creación de test plans
Definición de objetivos y scope
Hitos y cronograma
Gestión de recursos
Análisis de riesgos

6. Matriz de Trazabilidad (RTM) 🔗

Mapeo requisitos → test cases
Análisis de cobertura de requisitos
Identificación de requisitos sin pruebas
Reporte de gaps

7. Reportería Comprehensiva 📈

Dashboards en tiempo real
Reportes ejecutivos
Métricas de calidad
Análisis de tendencias
Exportación multi-formato

🏗️ Arquitectura

┌─────────────────────────────────────────────────────┐
│          Claude AI (via MCP Protocol)               │
└────────────────┬────────────────────────────────────┘
                 │
                 ▼
┌─────────────────────────────────────────────────────┐
│         QA MCP Server (TypeScript/Node.js)          │
├─────────────────────────────────────────────────────┤
│  Test Case Management      │  Defect Management     │
│  Test Execution Tracking   │  Coverage Analysis     │
│  Test Planning             │  RTM & Traceability    │
│  Reporting Engine          │  Statistics & Analytics│
└─────────────────────────────────────────────────────┘
                 │
    ┌────────────┼────────────┐
    ▼            ▼            ▼
[Database]  [File System]  [External Tools]
- Test Cases   - Reports    - Jenkins
- Results      - Logs       - GitHub
- Defects      - Artifacts  - JIRA

🔧 Instalación

Requisitos

Node.js 16+
TypeScript 4.5+
Claude API Key

Setup

# 1. Clonar o descargar el servidor
git clone <repo-url>
cd qa-mcp-server

# 2. Instalar dependencias
npm install

# 3. Compilar TypeScript
npm run build

# 4. Configurar en .claude/settings.json
cat > ~/.claude/settings.json << 'EOF'
{
  "mcpServers": {
    "qa": {
      "command": "node",
      "args": ["qa-mcp-server.js"],
      "env": {
        "API_KEY": "your-key-here"
      }
    }
  }
}
EOF

# 5. Iniciar el servidor
npm start

💡 Uso

Ejemplo 1: Crear un Test Case

User: "Crea un test case para validar el login de usuarios"

Claude:
Voy a crear un test case bien estructurado para validar el login...

Usando: create_test_case
{
  "title": "Validar login de usuario con credenciales correctas",
  "description": "Verifica que un usuario pueda hacer login exitosamente",
  "steps": [
    "1. Navegar a la página de login",
    "2. Ingresar email válido",
    "3. Ingresar contraseña correcta",
    "4. Hacer click en el botón Login",
    "5. Esperar redirección"
  ],
  "expected_result": "Usuario redirigido a dashboard, sesión activa",
  "priority": "critical",
  "tags": ["authentication", "core-feature", "login"]
}

✅ Creado: TC-1718527500 "Validar login de usuario con credenciales correctas"

Ejemplo 2: Registrar Resultado de Test

User: "Ejecuté el test TC-1718527500 y falló. El mensaje de error dice que no 
       se puede conectar a la base de datos de usuarios."

Usando: record_test_result
{
  "test_case_id": "TC-1718527500",
  "status": "failed",
  "actual_result": "Error al conectar a base de datos de usuarios",
  "executed_by": "qa_engineer_01",
  "environment": "staging",
  "duration": 12,
  "error_message": "Connection timeout: unable to reach auth_service on port 5432"
}

❌ Registrado: Test TC-1718527500 falló en staging

Ejemplo 3: Reportar Defecto

User: "El test falló porque el servicio de autenticación no responde. 
       Este es un bug crítico que encontramos en staging."

Usando: report_defect
{
  "title": "Auth service timeout - database connection issue",
  "description": "El servicio de autenticación no puede conectarse a la BD de usuarios.
                 Timeout después de 30 segundos. Afecta login de todos los usuarios.",
  "severity": "critical",
  "priority": "p0",
  "found_in": "staging",
  "detected_by": "qa_engineer_01",
  "related_test_case_ids": ["TC-1718527500"]
}

🐛 Reportado: DEF-1718527501
   Severidad: CRITICAL
   Prioridad: P0
   Estado: OPEN

Ejemplo 4: Generar Reporte QA

User: "Dame un reporte completo del estado actual del testing"

Usando: generate_qa_report

╔════════════════════════════════════════════════════════════════╗
║                  COMPREHENSIVE QA REPORT                      ║
║           Generated: 2026-06-16T10:30:00.000Z                 ║
╚════════════════════════════════════════════════════════════════╝

📊 TEST EXECUTION SUMMARY
────────────────────────────────────────────────────────────────
  Total Tests Executed:     145
  ✅ Passed:                128 (88%)
  ❌ Failed:                12
  🔒 Blocked:               3
  ⏭️  Skipped:               2

🐛 DEFECT SUMMARY
────────────────────────────────────────────────────────────────
  Total Defects:            15
  🟠 Open:                  7
  🟡 In Progress:           5
  ✅ Resolved:              3

  By Priority:
    P0: 2
    P1: 3
    P2: 6
    P3: 4

📋 REQUIREMENTS TRACEABILITY
────────────────────────────────────────────────────────────────
  Total Requirements:       32
  ✅ Fully Traced:          28
  🟡 Partially Traced:      3
  🔴 Untraced:              1
  Coverage:                 87%

📈 CODE COVERAGE
────────────────────────────────────────────────────────────────
  Overall Coverage:         78%
  Lines Covered:            3425/4390
  Uncovered Areas:          965

📚 API Reference

Test Case Management

`create_test_case`

Crea un nuevo caso de prueba.

create_test_case({
  title: string,                 // Título descriptivo del test
  description: string,           // Descripción detallada
  steps: string[],              // Pasos a seguir
  expected_result: string,      // Resultado esperado
  priority: "critical" | "high" | "medium" | "low",
  tags?: string[]               // Etiquetas opcionales
})
→ TestCase

`list_test_cases`

Lista test cases con filtros opcionales.

list_test_cases({
  priority?: string,
  tag?: string,
  status?: string
})
→ TestCase[]

`get_test_case`

Obtiene detalles de un test case específico.

get_test_case({
  test_case_id: string
})
→ TestCase | null

Test Execution

`record_test_result`

Registra el resultado de una ejecución de test.

record_test_result({
  test_case_id: string,
  status: "passed" | "failed" | "blocked" | "skipped",
  actual_result: string,
  executed_by: string,
  environment: string,
  duration: number,              // en segundos
  error_message?: string
})
→ TestResult

`get_execution_stats`

Obtiene estadísticas de ejecución de tests.

get_execution_stats()
→ {
  total: number,
  passed: number,
  failed: number,
  blocked: number,
  skipped: number,
  passRate: number
}

Defect Management

`report_defect`

Reporta un nuevo defecto/bug.

report_defect({
  title: string,
  description: string,
  severity: "critical" | "high" | "medium" | "low",
  priority: "p0" | "p1" | "p2" | "p3",
  found_in: string,             // environment donde se encontró
  detected_by: string,          // quien lo detectó
  related_test_case_ids?: string[]
})
→ Defect

`list_defects`

Lista defectos con filtros.

list_defects({
  severity?: string,
  status?: string,
  priority?: string
})
→ Defect[]

`update_defect`

Actualiza estado, asignación o resolución de un defecto.

update_defect({
  defect_id: string,
  status?: "open" | "in-progress" | "in-review" | "resolved" | "closed" | "reopened",
  assigned_to?: string,
  resolution?: string
})
→ Defect | null

`get_defect_stats`

Obtiene estadísticas de defectos.

get_defect_stats()
→ {
  total: number,
  open: number,
  inProgress: number,
  resolved: number,
  byPriority: Record<string, number>,
  bySeverity: Record<string, number>
}

Coverage Analysis

`get_coverage_report`

Genera o recupera reporte de cobertura.

get_coverage_report({
  generate?: boolean,
  total_lines?: number,
  covered_lines?: number,
  by_file?: Record<string, { covered: number, total: number }>
})
→ CoverageReport

Test Planning

`create_test_plan`

Crea un nuevo test plan.

create_test_plan({
  name: string,
  description: string,
  scope: string,
  objectives: string[],
  test_case_ids?: string[],
  start_date: string,           // ISO 8601
  end_date: string
})
→ TestPlan

Requirements Traceability

`add_requirement_to_rtm`

Añade un requisito a la matriz de trazabilidad.

add_requirement_to_rtm({
  requirement_id: string,
  description: string,
  test_case_ids?: string[],
  priority: string
})
→ RTMEntry

`get_rtm_report`

Obtiene reporte de trazabilidad de requisitos.

get_rtm_report()
→ {
  total: number,
  fullyTraced: number,
  partiallyTraced: number,
  untraced: number,
  coveragePercentage: number,
  entries: RTMEntry[]
}

Reporting

`generate_qa_report`

Genera reporte comprehensivo de QA.

generate_qa_report()
→ string (formatted report)

🎯 Casos de Uso

1. Ciclo de Testing Completo

Crear test plan → Crear test cases → Ejecutar tests → 
Registrar resultados → Reportar defectos → 
Actualizar defectos → Generar reportes

2. Gestión de Defectos Post-Release

Test ejecutado por usuario
    ↓
Defecto reportado → P0 asignado
    ↓
Desarrollador lo arregla → Status: in-review
    ↓
QA verifica fix → Status: resolved
    ↓
Incluido en retrospectiva

3. Análisis de Cobertura de Requisitos

RTM: REQ-123 mapeado a TC-456, TC-789
    ↓
Si todos los tests pasan → Requisito satisfecho
    ↓
Si algún test falla → Defecto vinculado a requisito
    ↓
Reporte de trazabilidad muestra gaps

4. Decisiones de Go/No-Go Release

Métricas observadas:
  - Pass Rate: 95% ✅
  - Critical Defects Open: 0 ✅
  - Requirement Coverage: 100% ✅
  - Code Coverage: 85% ✅
    
Decisión: ✅ READY FOR RELEASE

🔌 Integraciones

Integración con JIRA

// Cuando un defecto se reporta:
// 1. Se crea automáticamente en JIRA
// 2. Se sincroniza status bidireccionalmente
// 3. Se vinculan test cases relacionados

report_defect({...})
→ [DEF-123 creado en QA MCP]
→ [JIRA-456 creado automáticamente]
→ [Bidirectional sync habilitado]

Integración con GitHub

// Los test results se pueden postear como:
// 1. PR comments
// 2. Status checks
// 3. Commit statuses
// 4. Release notes

record_test_result({...})
→ [GitHub Check created]
→ [PR status actualizado]

Integración con CI/CD (Jenkins, GitHub Actions)

// Los tests se ejecutan en pipeline
// Los resultados se sincronizan automáticamente:

pipeline {
  post {
    always {
      // Post test results to QA MCP
      sh '''
        curl -X POST http://qa-mcp:3000/api/results \
          -H "Content-Type: application/json" \
          -d @test-results.json
      '''
    }
  }
}

Integración con Confluence

// Genera documentación automática:
// - Test Plans → Confluence pages
// - RTM Reports → Wiki
// - Execution reports → Living documentation

generate_qa_report()
→ [Confluence page creada]
→ [Automáticamente actualizada cada ejecución]

🚀 Mejores Prácticas

1. Test Case Design

✅ Cada test case debe probar UNA cosa ✅ Usar nombres descriptivos y claros ✅ Incluir precondiciones explícitas ✅ Especificar datos de entrada específicos ✅ Definir claramente el resultado esperado

2. Defect Reporting

✅ Describir en qué condiciones ocurre el bug ✅ Incluir pasos para reproducir ✅ Adjuntar evidencia (screenshots, logs) ✅ Vincular test cases relacionados ✅ Proporcionar stack traces cuando sea posible

3. Cobertura de Requisitos

✅ Mapear cada requisito a al menos un test ✅ Rastrear cambios de requisitos ✅ Mantener RTM actualizado ✅ Reportar gaps regularmente ✅ Revisar antes de cada release

4. Métricas Importante

📊 Métricas a Monitorear:
   - Pass Rate (objetivo > 95%)
   - Defect Density (máx 2 por 1000 LOC)
   - Code Coverage (objetivo > 80%)
   - Requirement Traceability (100%)
   - Mean Time to Resolution (MTTR)
   - Test Cycle Time

5. Comunicación

✅ Generar reportes diarios/semanales ✅ Usar dashboards en tiempo real ✅ Escalación automática de P0/Critical ✅ Retrospectivas post-release ✅ Sharing de lecciones aprendidas

📊 Ejemplo de Flujo Completo

Día 1: Planning
├─ create_test_plan
├─ add_requirement_to_rtm (REQ-1 → REQ-50)
└─ create_test_case (TC-1 → TC-200)

Día 2-5: Execution
├─ record_test_result (run batch 1)
├─ record_test_result (run batch 2)
└─ report_defect (encontrados: DEF-1 → DEF-15)

Día 6: Analysis
├─ get_execution_stats → 88% pass rate
├─ list_defects (severity: critical) → 2 defectos P0
├─ get_coverage_report → 78% coverage
└─ get_rtm_report → 95% requirement coverage

Día 7: Report
└─ generate_qa_report → ejecutivos, stakeholders

Decisión: GO/NO-GO basada en métricas

Si NO-GO:
├─ Developers arreglan defectos críticos
├─ Smoke test suite (TC subset)
└─ Re-evaluación

Si GO:
├─ Release pushed
├─ Post-release monitoring
└─ Retrospectiva

🛠️ Troubleshooting

Error: "Test case not found"

Verifica que el ID del test case sea correcto

list_test_cases() # para ver todos los IDs

Error: "Connection timeout"

Asegúrate que el MCP server esté corriendo

npm start

High False Positive Rate

Revisa la especificación del test case

¿Está bien definido el expected result?
¿Hay flakiness por timing?
¿Los datos de test son correctos?

📞 Soporte y Contribuciones

Para reportar issues o contribuir:

Abre un GitHub issue con detalles
Incluye logs y pasos para reproducir
Proporciona contexto del ambiente
Submit PR con fix propuesto

Made with ❤️ for Quality Assurance Engineering

Available Tools

15 tools

create_handoff_summaryC

Create clean handoff to start new session

ParametersJSON Schema

Name	Required	Description	Default
`ticket_id`	Yes
`current_stage`	Yes

TDQS

C2.6/5.0

Behavior2/5

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

No annotations provided, so description must disclose behavioral traits. It does not mention idempotency, authentication requirements, side effects, or what happens on duplicate ticket_ids.

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

Conciseness3/5

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

The description is a single sentence, making it concise, but it lacks structure and depth. It could be expanded with key information without losing brevity.

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?

Given the tool's role in QA workflows and the lack of output schema, the description is too minimal. It fails to explain what a 'clean handoff' entails or how the summary is used in the new session.

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

Parameters2/5

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

Schema coverage is 0%, and the description adds no meaning to ticket_id or current_stage. It does not explain how to determine current_stage or format ticket_id, leaving the agent guessing.

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 states the tool creates a handoff summary to start a new session, using a specific verb and resource. It distinguishes from siblings like create_session_checkpoint, though it could be more explicit about the content.

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?

No guidance on when to use this tool versus alternatives. With 14 sibling tools, the description should indicate when a handoff summary is appropriate instead of a checkpoint or test case.

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

create_session_checkpointC

Save a clean output at the end of each stage for next stage input

ParametersJSON Schema

Name	Required	Description	Default
`stage`	Yes
`output`	Yes
`ticket_id`	Yes
`tokens_used`	Yes
`output_quality`	No
`time_spent_seconds`	Yes

TDQS

C2.9/5.0

Behavior2/5

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

No annotations provided, so description carries full burden. It only states 'save a clean output' without disclosing whether it overwrites, requires specific permissions, or what happens on conflict. For a mutation tool, this is insufficient.

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

Conciseness3/5

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

Single sentence is concise but underinstructive for a tool with 6 parameters. Front-loads the core purpose but lacks structure like grouping or parameter explanations.

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?

Tool has 6 parameters, no output schema, and no annotations. Description fails to explain return value, error handling, or the meaning of 'clean output'. Incomplete for reliable agent invocation.

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

Parameters1/5

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

Schema coverage is 0% and description adds no meaning to any of the 6 parameters (ticket_id, stage, output, tokens_used, time_spent_seconds, output_quality). Agent gets no help understanding what each field means or how to fill them.

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?

Description clearly states the verb 'save' and resource 'output' with context 'at the end of each stage for next stage input'. It distinguishes from siblings like 'load_stage_checkpoint' which loads rather than saves.

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?

Implies use at end of stages but provides no explicit guidance on when to use versus alternatives like 'create_handoff_summary' or when not to use. No exclusions or prerequisites mentioned.

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

create_test_caseC

Create a new test case with title, description, steps, and expected result

ParametersJSON Schema

Name	Required	Description	Default
`tags`	No
`steps`	Yes
`title`	Yes
`priority`	Yes
`description`	Yes
`expected_result`	Yes

TDQS

C2.7/5.0

Behavior2/5

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

With no annotations, the description carries full burden. It only states creation but omits side effects, permissions, or whether it overwrites existing data. No behavioral context beyond the verb.

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 concise sentence with no redundancy. However, it omits important parameter details, which would justify a slightly lower score.

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?

Given 6 parameters, no output schema, and no annotations, the description fails to provide enough context for an agent to invoke the tool correctly without external knowledge.

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

Parameters2/5

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

Schema coverage is 0%, and description only lists four of six parameters (missing tags and priority). It adds no detail on types, formats, or constraints beyond what is in the schema.

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 states the action 'Create' and the resource 'test case', listing key fields. However, it does not differentiate from sibling tools like 'save_test_cases' or 'find_reusable_test_cases', though the task is distinct.

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?

No guidance on when to use this tool versus alternatives such as 'save_test_cases' or 'report_defect'. No prerequisites or context provided.

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

find_reusable_test_casesD

Find existing test cases for reuse

ParametersJSON Schema

Name	Required	Description	Default
`ticket_id`	Yes
`filter_tags`	No
`filter_priority`	No

TDQS

D1.8/5.0

Behavior1/5

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

With no annotations, the description carries full burden but mentions nothing about behavior (e.g., read-only, destructive, auth needs). The tool might be a read operation, but that is not stated, making it unsafe for the agent to use.

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

Conciseness2/5

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

The description is a single short sentence, which is under-specification rather than conciseness. It fails to convey essential information given the tool has 3 parameters and no other structured documentation.

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

Completeness1/5

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

The tool lacks output schema, parameter descriptions, and behavioral information. For a search/find operation with multiple filters, the description should provide details on expected returns, but it provides none.

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

Parameters1/5

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

The input schema has 0% parameter description coverage, and the description does not explain what 'ticket_id', 'filter_tags', or 'filter_priority' mean or how they affect results. The agent gets no semantic help beyond the parameter names.

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

Purpose3/5

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

The description 'Find existing test cases for reuse' provides a general idea but lacks specificity. It doesn't differentiate from siblings like 'save_test_cases' or 'create_test_case' which could also involve existing cases. The purpose is vague, not clearly indicating the scope or distinction.

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?

No guidance is provided on when to use this tool versus alternatives. The description lacks context about what scenarios this tool is appropriate for, leaving the agent without decision support.

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

generate_qa_reportB

Generate comprehensive QA report with all metrics and statistics

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

B3.2/5.0

Behavior2/5

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

No annotations are provided, so the description carries full burden. It states only that the tool generates a report with 'all metrics and statistics', but does not disclose behavioral traits such as whether it modifies data, requires authentication, or has performance implications. For a report generator, read-only nature is implied but not explicit.

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 a single, concise sentence with no superfluous words. It is front-loaded with the key action and resource, making it quickly scannable. Every part earns its place.

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?

Given zero parameters and no output schema, the description is minimally complete but lacks detail on what 'comprehensive' entails or the output format. Sibling tools suggest a testing context, but the description does not connect to the broader workflow or specify if the report is interactive or a file.

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

Parameters4/5

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

There are no parameters, so schema coverage is 100% and baseline is 4. The description adds meaning beyond the schema by specifying 'all metrics and statistics', which clarifies the output scope. No further parameter detail is needed.

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 states the verb 'generate' and the resource 'QA report', indicating a distinct purpose. It is not a tautology and distinguishes from sibling tools that focus on individual test cases or defects, though it could be more specific about the report scope.

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?

No guidance on when to use this tool versus alternatives. The description does not mention conditions, prerequisites, or scenarios where it is appropriate. It fails to differentiate from other tools like 'validate_qa_output' or 'create_handoff_summary'.

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

get_model_recommendationC

Get recommended model (Sonnet for analysis, Haiku for execution)

ParametersJSON Schema

Name	Required	Description	Default
`stage`	Yes

TDQS

C2.9/5.0

Behavior2/5

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

Without annotations, the description must disclose behavior. It only hints at a mapping (analysis vs execution) but omits details like output format, side effects, or whether the recommendation is static. An agent cannot infer response structure or caching behavior.

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. However, it omits critical details that would make it maximally useful, sacrificing completeness for brevity.

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?

Given low schema coverage, no output schema, and no annotations, the description is insufficient. It fails to explain return values, the exact model for each stage, or edge cases. An agent would need to infer or test to use it reliably.

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

Parameters2/5

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

Schema coverage is 0%, so the description should explain parameter semantics. It mentions 'Sonnet for analysis, Haiku for execution' but does not map individual stage enum values to analysis or execution, leaving ambiguity (e.g., is 'test-case-generation' analysis or execution?). The description adds minimal value over the enum list.

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's purpose: 'Get recommended model' with a specific verb and resource. It distinguishes from sibling tools like 'get_recommended_prompt' by explicitly focusing on model selection (Sonnet vs Haiku) for different QA stages.

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?

No guidance is provided on when to use this tool versus alternatives such as 'get_recommended_prompt' or 'get_stage_config'. The description does not specify prerequisites or typical call contexts.

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

get_recommended_promptC

Get the recommended prompt structure for a specific stage

ParametersJSON Schema

Name	Required	Description	Default
`stage`	Yes

TDQS

C2.8/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden. The minimal description ('Get the recommended prompt structure for a specific stage') does not disclose any behavioral traits, such as whether the tool is read-only, requires authorization, or what the response contains. This is insufficient for a tool with zero annotation coverage.

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

Conciseness3/5

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

The description is a single sentence, making it very concise. However, it sacrifices informativeness for brevity. While every sentence earns its place, the content is largely redundant with the tool name and schema. A fully informative description would be slightly longer.

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?

Given there are no annotations, no output schema, and only one parameter, the description is too minimal. It fails to explain what the prompt structure is used for, what the return value looks like, or any additional context needed for correct invocation. The tool is part of a suite of QA tools, but the description does not leverage that 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 0%, so the description adds no parameter details. However, the input schema itself is well-defined with a single required parameter 'stage' and an enum of six clearly named stages (e.g., 'ticket-analysis', 'test-planning'), which are self-explanatory. The description does not add extra meaning, but the schema compensates somewhat. A score of 3 reflects that no additional value is provided beyond the schema.

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 states 'Get the recommended prompt structure for a specific stage', which clearly identifies the verb ('Get') and the resource ('recommended prompt structure'). It distinguishes from siblings like 'get_stage_config' and 'get_model_recommendation' by focusing on prompt structure. However, it lacks specificity about what the prompt is used for, especially given the QA context.

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?

No guidance is provided on when to use this tool versus alternatives. There is no explicit context, exclusions, or mention of prerequisites. The description is too terse to help an agent decide between this and sibling tools like 'get_stage_config' or 'get_model_recommendation'.

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

get_stage_configC

Get configuration and recommendations for a specific stage

ParametersJSON Schema

Name	Required	Description	Default
`stage`	Yes

TDQS

C2.6/5.0

Behavior2/5

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

With no annotations, the description must fully disclose behaviors. It only states it 'gets' information, implying a non-destructive read, but omits idempotency, auth requirements, or side effects. More detail is needed for safe agent use.

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

Conciseness3/5

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

The single-sentence description is concise with no fluff, earning base credit. However, it lacks structure or front-loading of key usage info, making it adequate but not well-organized.

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 simple tool with one enum parameter and no output schema, the description is too minimal. It fails to specify the nature of the 'configuration and recommendations,' leaving an agent uncertain about the return value and how to interpret results.

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

Parameters2/5

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

Schema coverage is 0%, so the description should explain parameter meanings beyond the self-explanatory enum values. It merely mentions 'stage' implicitly, adding no insight into what configuration or recommendations are returned for each stage.

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 states the tool retrieves configuration and recommendations for a specific stage, which is a distinct read operation. The verb 'get' and resource 'configuration and recommendations' are specific, and the enum of stages differentiates it from sibling tools that create, save, or report.

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?

The description provides no guidance on when to use this tool versus siblings like get_model_recommendation or get_recommended_prompt. It lacks context on prerequisites or alternative tools for similar tasks.

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

load_stage_checkpointC

Load clean output from previous stage (not full chat)

ParametersJSON Schema

Name	Required	Description	Default
`stage`	Yes
`ticket_id`	Yes

TDQS

C2.6/5.0

Behavior2/5

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

No annotations provided, so description carries full burden. It fails to disclose side effects, idempotency, authentication needs, or what 'clean output' entails (e.g., format, structure).

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?

Single sentence with no filler. Parenthetical clarification 'not full chat' adds essential context efficiently. Perfectly concise and front-loaded.

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

Completeness1/5

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

Given 2 required params, no output schema, and no annotations, the description is severely incomplete. It omits return format, preconditions, and what 'clean output' means for downstream use.

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

Parameters1/5

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

Schema description coverage is 0%. Description does not explain what 'ticket_id' or 'stage' represent, nor acceptable values or formats. Both parameters are undocumented beyond the schema.

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?

Description uses specific verb 'load' and resource 'clean output from previous stage', clearly distinguishing from full chat. However, it does not explicitly differentiate from sibling tools like create_session_checkpoint or get_stage_config.

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?

No guidance on when to use this tool versus alternatives. The 'not full chat' hint is implicit but insufficient for deciding between nine sibling tools.

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

record_test_resultC

Record the result of a test execution

ParametersJSON Schema

Name	Required	Description	Default
`status`	Yes
`duration`	Yes
`environment`	Yes
`executed_by`	Yes
`test_case_id`	Yes
`actual_result`	Yes
`error_message`	No

TDQS

C2.4/5.0

Behavior2/5

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

No annotations are provided, so the description must fully disclose behavioral traits. It does not indicate side effects (e.g., that this is a write operation that creates a new record), required permissions, rate limits, or whether the operation is idempotent. The minimal description fails to add cautionary or operational context.

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

Conciseness3/5

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

The description is one efficient sentence with no wasted words, which aligns with conciseness. However, it sacrifices necessary detail—for a tool with many parameters and siblings, brevity leads to inadequacy. It earns a middle score for being clean but under-informative.

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

Completeness1/5

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

The tool has 7 parameters (6 required), no output schema, no annotations, and several sibling tools. The single-sentence description leaves the agent with insufficient context to use the tool correctly. It does not explain the meaning of each parameter, required input formats, or what happens upon success/failure. This is critically incomplete.

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

Parameters1/5

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

Schema description coverage is 0%, meaning none of the 7 parameters (6 required) are explained in the description. The description adds no meaning beyond the raw schema, so the agent must infer the purpose of each field. This is a critical gap given the number and requirements.

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 uses a specific verb-resource pair ('record the result of a test execution') which makes the general purpose clear. However, it lacks additional details that would distinguish it from sibling tools like 'report_defect' or 'validate_qa_output', which might also involve recording results. A more precise scope would elevate it to a 5.

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?

No guidance on when to use this tool versus alternatives is provided. The description does not mention prerequisites, when not to use it, or suggest any sibling tools for different scenarios. This leaves the agent without decision-making support.

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

report_defectC

Report a new defect/bug found during testing

ParametersJSON Schema

Name	Required	Description	Default
`title`	Yes
`found_in`	Yes
`priority`	Yes
`severity`	Yes
`description`	Yes
`detected_by`	Yes
`related_test_case_ids`	No

TDQS

C2.6/5.0

Behavior2/5

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

No annotations are provided, placing the full burden on the description. It only indicates a mutate action (reporting) but omits critical behavioral details such as side effects, idempotency, required permissions, or what happens after reporting.

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

Conciseness3/5

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

The description is a single sentence of 7 words, which is concise but too brief for a tool with 7 parameters and no other documentation. It says nothing about how to structure input or expected output.

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

Completeness1/5

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

Given the complexity (7 parameters, no output schema, no annotations), the description is grossly incomplete. It fails to convey any behavioral context, parameter usage, or return expectations, leaving the agent with little confidence.

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

Parameters1/5

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

With 0% schema description coverage, the description must compensate but does not add any meaning beyond parameter names. The schema has 7 parameters (6 required), yet the description merely states the tool's purpose without explaining fields like 'title', 'severity', or 'related_test_case_ids'.

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 verb 'Report', the resource 'a new defect/bug', and the context 'during testing'. It is specific and differentiates from sibling tools like 'record_test_result' which likely focus on test outcomes.

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?

The description provides no guidance on when to use this tool versus alternatives (e.g., 'record_test_result' or 'create_test_case'). There is no mention of prerequisites, exclusions, or usage context.

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

save_test_casesC

Save test cases for future reuse on bug fixes/regressions

ParametersJSON Schema

Name	Required	Description	Default
`ticket_id`	Yes
`test_cases`	Yes

TDQS

C2.4/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden of disclosing behavioral traits. It only states the purpose ('save test cases') but does not reveal whether the operation is creating, updating, or overwriting, nor does it mention side effects, permissions, or error conditions. This is insufficient for an agent to predict the tool's impact.

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

Conciseness3/5

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

The description is a single concise sentence with no redundant words, but it sacrifices essential detail for brevity. While front-loaded with the action, it does not earn its place by providing sufficient information, resulting in a minimal but incomplete description.

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?

Given the lack of annotations, output schema, and parameter guidance, the description is far from complete. The tool handles structured data (array of test cases with multiple fields) and requires context about validation, return values, and error handling to be used effectively. The current description leaves critical gaps.

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

Parameters1/5

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

With schema description coverage at 0%, the description adds no meaning to the parameters. It does not explain what 'ticket_id' represents or describe the structure of 'test_cases' (e.g., the significance of fields like 'id', 'steps', or 'priority'). An agent must rely solely on the raw schema, which lacks explanatory context.

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 uses the verb 'save' and identifies the resource as 'test cases', clearly indicating the tool's primary action. It also specifies the context 'for future reuse on bug fixes/regressions', giving a sense of purpose. However, it does not differentiate between saving a single test case (as might be done by create_test_case) or a batch, leaving some ambiguity in its scope.

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?

The description provides no explicit guidance on when to use this tool versus alternatives like create_test_case or find_reusable_test_cases. It implies usage for saving test cases for later use but offers no exclusions, prerequisites, or comparisons with sibling tools, leaving an AI agent without decision support.

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

validate_context_healthC

Check if context is clean and not bloated

ParametersJSON Schema

Name	Required	Description	Default
`ticket_id`	Yes

TDQS

C2.4/5.0

Behavior2/5

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

With no annotations provided, the description must bear the full burden of behavioral disclosure. It implies a read-only check but does not explicitly state side effects, prerequisites, or safety profile. The brief description is insufficient for safe agent invocation.

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

Conciseness3/5

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

The description is extremely concise—a single phrase—but lacks structure or additional context. While brevity is valued, it sacrifices informativeness, making it minimally adequate.

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?

Given the tool's simplicity (one required parameter, no output schema), the description fails to clarify return values, interpretation of results, or what constitutes 'clean' vs. 'bloated'. The agent lacks critical context for effective use.

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

Parameters1/5

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

Schema description coverage is 0%, and the description adds no information about the 'ticket_id' parameter beyond its type. The agent cannot infer format, purpose, or constraints from the description alone.

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 'Check if context is clean and not bloated' clearly states the tool's action (check) and target (context health). It is specific enough to distinguish from siblings, though 'clean' and 'bloated' lack precise definition.

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?

No usage guidance is provided. The description does not indicate when to use this tool over sibling validators like 'validate_execution_readiness' or 'validate_qa_output', nor does it mention scenarios where this tool is preferable.

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

validate_execution_readinessC

Check if test cases are ready for execution

ParametersJSON Schema

Name	Required	Description	Default
`has_test_data`	No
`critical_count`	Yes
`test_case_count`	Yes

TDQS

C2.4/5.0

Behavior2/5

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

With no annotations, the description must disclose behavioral traits, but it only says 'check', which is ambiguous. It does not clarify whether the tool is read-only, requires side effects, or what happens upon invocation. This is insufficient for safe agent use.

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

Conciseness3/5

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

The description is a single sentence, which is concise, but it is under-specified. It earns its place by stating the core purpose, but additional structured detail is missing. It could be longer to add value while remaining efficient.

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

Completeness1/5

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

Given no annotations, no output schema, and three parameters with zero coverage, the description is severely incomplete. It does not explain return values, validation logic, or how to interpret results, making it inadequate for effective agent use.

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

Parameters1/5

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

Schema description coverage is 0%, and the tool description fails to explain any of the three parameters (test_case_count, critical_count, has_test_data). The agent cannot infer their meaning or constraints from the provided text.

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 'Check if test cases are ready for execution' clearly states the verb (check) and resource (test cases readiness), making the purpose understandable. However, it does not differentiate from sibling tools like validate_context_health, which might also assess readiness.

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?

No guidance is provided on when to use this tool versus alternatives. The description lacks context about prerequisites, conditions, or scenarios where this tool is appropriate, leaving the agent without decision support.

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

validate_qa_outputD

Validate output before moving to next stage

ParametersJSON Schema

Name	Required	Description	Default
`stage`	Yes
`output`	Yes
`require_validation`	No

TDQS

D1.8/5.0

Behavior1/5

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

No annotations are provided, so the description must disclose behavioral traits. It does not mention any side effects, authentication needs, or what happens on failure. The description is too minimal to convey any behavioral transparency.

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

Conciseness2/5

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

The description is extremely concise at one sentence, but it is under-specified. Conciseness is positive only when paired with sufficient information; here it sacrifices completeness.

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

Completeness1/5

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

Given the presence of many sibling validation tools and the lack of an output schema, this description is completely inadequate. It does not explain return values, behavior, or when to use this tool, making it nearly useless for an AI agent.

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

Parameters1/5

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

The schema has three parameters with no descriptions (0% coverage), and the description adds no meaning to them. It does not explain what stage, output, or require_validation mean, leaving the agent without essential usage details.

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

Purpose3/5

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

The description states the tool validates output before moving to the next stage, giving a clear verb and resource. However, it lacks specificity about what kind of output and what validation entails, and it does not distinguish from similar sibling tools like validate_context_health or validate_execution_readiness.

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?

No guidance is provided about when to use this tool versus alternatives. The description does not mention prerequisites, exclusions, or context for its use among the many sibling validation tools.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

15 tool updatesv1.0.0Jun 28, 2026
- First observedcreate_handoff_summary
- First observedcreate_session_checkpoint
- First observedcreate_test_case
- First observedfind_reusable_test_cases
- First observedgenerate_qa_report
- First observedget_model_recommendation
- First observedget_recommended_prompt
- First observedget_stage_config
- First observedload_stage_checkpoint
- First observedrecord_test_result
- First observedreport_defect
- First observedsave_test_cases
- First observedvalidate_context_health
- First observedvalidate_execution_readiness
- First observedvalidate_qa_output

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

B3/5.0

Disambiguation5/5

Each tool targets a distinct aspect of QA workflow (handoff, checkpoints, test cases, reporting, recommendations, validations). No two tools appear to do the same thing.

Naming Consistency5/5

All tools follow a consistent verb_noun snake_case pattern (e.g., create_test_case, validate_context_health), making it easy to predict functionality.

Tool Count5/5

15 tools is well-scoped for a QA server covering test creation, execution, reporting, defect tracking, and stage management. Each tool serves a clear purpose.

Completeness4/5

Core workflows (create, find, execute, report, validate) are covered. Minor gaps include missing update/delete operations for test cases, but the surface is sufficient for the intended structured process.

Maintenance

ActivityStale

ResponsivenessNo issues

Resources

GitHub Repository

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