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Spec Workflow MCP

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log-implementation

Record implementation details for completed tasks to create a searchable knowledge base that prevents code duplication and helps future AI agents discover existing artifacts.

Instructions

Record comprehensive implementation details for a completed task.

⚠️ CRITICAL: Artifacts are REQUIRED. This creates a searchable knowledge base that future AI agents use to discover existing code and avoid duplication.

WHY DETAILED LOGGING MATTERS

Future AI agents (and future you) will use grep/ripgrep to search implementation logs before implementing new tasks. Complete logs prevent:

  • ❌ Creating duplicate API endpoints

  • ❌ Reimplementing existing components

  • ❌ Duplicating utility functions and business logic

  • ❌ Breaking established integration patterns

Incomplete logs = Duplicated code = Technical debt

REQUIRED FIELDS

artifacts (REQUIRED - Object)

Contains structured data about what was implemented. Must include relevant artifact types:

apiEndpoints (array of API endpoint objects)

When new API endpoints are created/modified, document:

  • method: HTTP method (GET, POST, PUT, DELETE, PATCH)

  • path: Route path (e.g., "/api/specs/:name/logs")

  • purpose: What this endpoint does

  • requestFormat: Request body/query params format (JSON schema or example)

  • responseFormat: Response structure (JSON schema or example)

  • location: File path and line number (e.g., "src/server.ts:245")

Example:

{
  "method": "GET",
  "path": "/api/specs/:name/implementation-log",
  "purpose": "Retrieve implementation logs with optional filtering",
  "requestFormat": "Query params: taskId (string, optional), search (string, optional)",
  "responseFormat": "{ entries: ImplementationLogEntry[] }",
  "location": "src/dashboard/server.ts:245"
}

components (array of component objects)

When reusable UI components are created, document:

  • name: Component name

  • type: Framework type (React, Vue, Svelte, etc.)

  • purpose: What the component does

  • location: File path

  • props: Props interface or type signature

  • exports: What it exports (array of export names)

Example:

{
  "name": "LogsPage",
  "type": "React",
  "purpose": "Main dashboard page for viewing implementation logs with search and filtering",
  "location": "src/modules/pages/LogsPage.tsx",
  "props": "{ specs: any[], selectedSpec: string, onSelect: (value: string) => void }",
  "exports": ["LogsPage (default)"]
}

functions (array of function objects)

When utility functions are created, document:

  • name: Function name

  • purpose: What it does

  • location: File path and line

  • signature: Function signature (params and return type)

  • isExported: Whether it can be imported

Example:

{
  "name": "searchLogs",
  "purpose": "Search implementation logs by keyword",
  "location": "src/dashboard/implementation-log-manager.ts:156",
  "signature": "(searchTerm: string) => Promise<ImplementationLogEntry[]>",
  "isExported": true
}

classes (array of class objects)

When classes are created, document:

  • name: Class name

  • purpose: What the class does

  • location: File path

  • methods: List of public methods

  • isExported: Whether it can be imported

Example:

{
  "name": "ImplementationLogManager",
  "purpose": "Manages CRUD operations for implementation logs",
  "location": "src/dashboard/implementation-log-manager.ts",
  "methods": ["loadLog", "addLogEntry", "getAllLogs", "searchLogs", "getTaskStats"],
  "isExported": true
}

integrations (array of integration objects)

Document how frontend connects to backend:

  • description: How components connect to APIs

  • frontendComponent: Which component initiates the connection

  • backendEndpoint: Which API endpoint is called

  • dataFlow: Describe the data flow (e.g., "User clicks → API call → State update → Re-render")

Example:

{
  "description": "LogsPage fetches logs via REST API and subscribes to WebSocket for real-time updates",
  "frontendComponent": "LogsPage",
  "backendEndpoint": "GET /api/specs/:name/implementation-log",
  "dataFlow": "Component mount → API fetch → Display logs → WebSocket subscription → Real-time updates on new entries"
}

GOOD EXAMPLE (Include ALL relevant artifacts)

Task: "Implemented logs dashboard with real-time updates"

{
  "taskId": "2.3",
  "summary": "Implemented real-time implementation logs dashboard with filtering, search, and WebSocket updates",
  "artifacts": {
    "apiEndpoints": [
      {
        "method": "GET",
        "path": "/api/specs/:name/implementation-log",
        "purpose": "Retrieve implementation logs with optional filtering",
        "requestFormat": "Query params: taskId (string, optional), search (string, optional)",
        "responseFormat": "{ entries: ImplementationLogEntry[] }",
        "location": "src/dashboard/server.ts:245"
      }
    ],
    "components": [
      {
        "name": "LogsPage",
        "type": "React",
        "purpose": "Main dashboard page for viewing implementation logs with search and filtering",
        "location": "src/modules/pages/LogsPage.tsx",
        "props": "None (uses React Router params)",
        "exports": ["LogsPage (default)"]
      }
    ],
    "classes": [
      {
        "name": "ImplementationLogManager",
        "purpose": "Manages CRUD operations for implementation logs",
        "location": "src/dashboard/implementation-log-manager.ts",
        "methods": ["loadLog", "addLogEntry", "getAllLogs", "searchLogs", "getTaskStats"],
        "isExported": true
      }
    ],
    "integrations": [
      {
        "description": "LogsPage fetches logs via REST API and subscribes to WebSocket for real-time updates",
        "frontendComponent": "LogsPage",
        "backendEndpoint": "GET /api/specs/:name/implementation-log",
        "dataFlow": "Component mount → API fetch → Display logs → WebSocket subscription → Real-time updates on new entries"
      }
    ]
  },
  "filesModified": ["src/dashboard/server.ts"],
  "filesCreated": ["src/modules/pages/LogsPage.tsx"],
  "statistics": { "linesAdded": 650, "linesRemoved": 15, "filesChanged": 2 }
}

BAD EXAMPLE (Don't do this)

❌ Empty artifacts - Future agents learn nothing:

{
  "taskId": "2.3",
  "summary": "Added endpoint and page",
  "artifacts": {},
  "filesModified": ["server.ts"],
  "filesCreated": ["LogsPage.tsx"]
}

❌ Vague summary with no structured data:

{
  "taskId": "2.3",
  "summary": "Implemented features",
  "artifacts": {},
  "filesModified": ["server.ts", "app.tsx"]
}

Instructions

  1. After completing a task, review what you implemented

  2. Identify all artifacts (APIs, components, functions, classes, integrations)

  3. Document each with full details and locations

  4. Include ALL the information - be thorough!

  5. Future agents depend on this data quality

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
projectPathNoAbsolute path to the project root (optional - uses server context path if not provided)
specNameYesName of the specification
taskIdYesTask ID (e.g., "1", "1.2", "3.1.4")
summaryYesBrief summary of what was implemented
filesModifiedYesList of files that were modified
filesCreatedYesList of files that were created
statisticsYesCode statistics for the implementation
artifactsYesREQUIRED: Structured data about implemented artifacts (APIs, components, functions, classes, integrations). See tool description for detailed format.

Tool Definition Quality

A4.7/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It thoroughly explains that this tool creates persistent records for future searchability, emphasizes the critical requirement of artifacts, and details the consequences of misuse (technical debt from duplication). It also implicitly indicates this is a write operation (recording details) without contradicting any annotations.

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 overly verbose with extensive examples, markdown formatting, and repetitive emphasis. While the front-loaded purpose is clear, the length (multiple sections like '# WHY DETAILED LOGGING MATTERS', '# REQUIRED FIELDS', examples) reduces conciseness. Some content (e.g., detailed bad examples) could be condensed without losing clarity.

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

Completeness5/5

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

Given the tool's complexity (8 parameters, nested objects) and lack of annotations/output schema, the description is highly complete. It covers the tool's purpose, usage context, parameter details (especially for 'artifacts'), behavioral implications, and examples. The only minor gap is no explicit mention of authentication or error handling, but this is reasonable for a logging tool.

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?

Schema description coverage is 100%, so the baseline is 3. The description adds significant value by elaborating on the 'artifacts' parameter with detailed sub-field requirements (e.g., apiEndpoints, components), examples, and formatting guidelines. However, it doesn't provide additional context for other parameters like 'projectPath' or 'statistics' beyond what the schema already describes.

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 explicitly states the tool's purpose: 'Record comprehensive implementation details for a completed task' and emphasizes creating 'a searchable knowledge base that future AI agents use to discover existing code and avoid duplication.' This clearly distinguishes it from sibling tools like approvals or spec-status, which appear unrelated to logging implementation artifacts.

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

Usage Guidelines5/5

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

The description provides explicit guidance on when to use this tool: 'After completing a task, review what you implemented' and 'Identify all artifacts (APIs, components, functions, classes, integrations).' It also includes strong warnings about when not to use it (e.g., 'Incomplete logs = Duplicated code = Technical debt') and contrasts good vs. bad examples to guide proper usage.

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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