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mcp-spec-comments

mcp-spec-comments

by erabu-yasumune
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
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pass_to_ai_for_implementation

Send commented files to AI for implementation. It reads @spec-impl markers, implements the code, and marks tasks as DONE after completion.

Instructions

コメント付きファイルをAIに渡して実装させる（ワークフローの最後のステップ）。@spec-implマーカーに従って実装し、完了後はDONE状態に更新する。

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": {
      "target_files": "<target_files>"
    },
    "name": "pass_to_ai_for_implementation"
  }
}

Input Schema

NameRequiredDescriptionDefault
target_filesYes実装対象のファイルパス配列
implementation_orderNo実装順序（省略可能）

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations provided, the description carries the full transparency burden. It discloses that the tool updates files to a DONE state after implementation, which is a key side effect. It also mentions following @spec-impl markers. It does not detail all potential side effects (e.g., file modifications, error handling), but the main behavioral traits are covered.

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, well-structured sentence that front-loads the primary purpose and includes workflow context. Every clause earns its place, with no redundant information. It is appropriately concise for the tool's complexity.

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

Completeness4/5

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

The description covers the essential behavior—passing files, implementing per marker, and updating to DONE—which is sufficient for a simple workflow step. There is no output schema, but the description does not need to explain return values. It lacks details on failure modes or what the DONE state entails, but overall it is reasonably complete given the tool's scope.

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 coverage is 100%, so baseline is 3. The description adds meaningful context beyond the schema by indicating that target_files should contain comments and that implementation follows @spec-impl markers. This enriches the plain 'implementation target files' definition with domain-specific semantics.

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 it passes commented files to AI for implementation, with a specific verb ('実装させる') and resource (commented files). It also positions itself as the workflow's final step and references the @spec-impl marker, distinguishing it from sibling tools like pass_to_ai_for_requirements and pass_to_ai_for_design.

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

Usage Guidelines4/5

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

The description explicitly mentions it is the workflow's last step, giving clear usage context and implying it should be used after other pass_to_ai steps. It does not explicitly name alternatives or provide when-not conditions, but the workflow positioning and sibling tool names make the intended usage clear.

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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