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haandol
by haandol

ingest_design_doc_draft

Store the client-generated design draft (theme, tone, page requests) and color theme by saving it as DESIGN.md for the project.

Instructions

Ingests the client-generated DESIGN.md draft and saves DESIGN.md.

Call AFTER prepare_design_doc_draft with the draft JSON you generated.

Args: project_id: Project ID (required). draft_json: Draft JSON (theme + tone + page_requests) from the client. color_theme: Color theme (stored into the theme).

Returns: JSON with project_id and design_doc_path.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
draft_jsonYes
project_idYes
color_themeNodark

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states that the tool saves DESIGN.md and returns a JSON with project_id and design_doc_path, but it does not mention side effects (e.g., overwriting behavior), permissions required, or whether the operation is reversible. This leaves gaps for an AI agent.

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 concise: two sentences for purpose and sequence, then bullet-like args and return. Every sentence adds value without redundancy. It is front-loaded with the core action and usage hint.

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?

Given moderate complexity (3 params, 2 required) and existence of an output schema, the description covers the essential: what it does, when to call, parameter details, and return shape. It could be improved by mentioning potential errors or idempotency, but the context is largely complete.

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?

The input schema has 0% coverage, but the description adds meaning beyond the raw schema. It explains that draft_json contains 'theme + tone + page_requests' and comes from the client, and that color_theme is 'stored into the theme'. This helps the agent understand the parameter content and source, compensating for the schema's lack of detail.

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 'Ingests the client-generated DESIGN.md draft and saves DESIGN.md.' It specifies the action (ingest and save) and the resource (DESIGN.md draft/file). It also distinguishes itself from siblings by explicitly saying 'Call AFTER prepare_design_doc_draft', which ties it to a specific preceding tool.

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 provides clear context by stating 'Call AFTER prepare_design_doc_draft with the draft JSON you generated.' This indicates the correct sequence. It does not explicitly list when not to use it, but the temporal dependency is sufficient guidance.

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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