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JamesZor

Antigravity MCP Server

by JamesZor

propose_design_questions

Generates clarifying questions and draft requirements to refine a project brief, ensuring clear requirements before code review or design work.

Instructions

Draft clarifying questions and candidate requirements to sharpen a build/improvement brief.

The first step of the Architect pipeline (the *grill*). Mirrors propose_research_questions
but for software projects: offloads brainstorming the requirements interview to a cheap
agy model. Returns clarifying questions (each with answer options) the orchestrator should
put to the user, plus a draft set of requirements. The orchestrator asks the user, refines,
and writes requirements.md before spending quota on a code review or design doc.

Returns JSON: {"clarifying_questions": [{"question", "why", "options": [...]}],
"draft_requirements": [...]}. Falls back to raw text if the model returns non-JSON.

Args:
    goal: The project goal — what to build or improve.
    repo_path: Optional path to an existing codebase being improved (gives the model context).
    context: Optional extra context (users, constraints, deadline, what already exists).
    tier: Model tier for the brainstorm (default 'flash' — this is a cheap task).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
goalYes
tierNoflash
contextNo
repo_pathNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations provided, the description carries full burden. It discloses the return format (JSON or fallback to raw text), the pipeline flow (orchestrator asks user, refines, writes requirements.md), and notes that the task is cheap (flash tier). However, it does not explicitly state whether the tool has side effects (though it is likely read-only).

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 well-structured with a summary, context, return details, and args. It is informative but slightly verbose with non-essential phrases like 'the *grill*'. Still, it efficiently conveys necessary information.

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 the tool has four parameters and an output schema, the description covers the return format, fallback, and pipeline. It does not detail the output schema fields, but this is acceptable since the output schema exists. The description is complete for an agent to use the tool effectively.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must compensate. It explains all four parameters: goal (required), repo_path (optional path to existing codebase), context (optional extra context), and tier (default 'flash' and described as cheap task). This adds meaningfully beyond the schema.

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 'draft' and the resource 'clarifying questions and candidate requirements to sharpen a build/improvement brief'. It distinguishes itself from the sibling tool 'propose_research_questions' by specifying it is for software projects.

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?

Explicitly states it is the first step of the Architect pipeline and that the orchestrator should use it before spending quota on code review or design doc. The description also notes it mirrors propose_research_questions but for software, providing clear usage context and alternatives.

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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