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faf_go

Identifies missing fields in a project context, asks questions until all required information is provided, then applies answers to achieve a gold code score.

Instructions

🎯 Guided interview to Gold Code - Claude asks questions till you hit 100%! Returns questions for missing fields, then apply answers to reach Gold Code 🧡⚡️

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathNoProject path. Sets session context for subsequent calls.
answersNoAnswers to apply. Keys are field paths (e.g., "project.goal", "human_context.why"), values are the answers. If provided, applies answers and returns new score.
Behavior3/5

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

Annotations indicate it's not read-only (consistent with 'apply answers'), but the description adds the interactive behavior (asking questions) and returning questions. However, it lacks details on side effects (e.g., whether answers overwrite existing fields) or any destructive potential beyond what annotations say (destructiveHint=false).

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 emojis, conveying the core idea without waste. It is front-loaded with the main purpose. Slightly informal but effective.

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 no output schema, the description mentions return values (questions, new score) but not their structure. It lacks context on what 'Gold Code' is, what triggers the interview, and whether prerequisites like initialization exist. Adequate but incomplete for a complex interactive tool.

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 covers both parameters completely (100% coverage). The description adds context about the two-step workflow (first call returns questions, second applies answers), which helps interpret parameter usage but does not add new semantic meaning 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 clearly states it's a guided interview to achieve 'Gold Code', with a specific verb+resource (interview to Gold Code). However, it doesn't explicitly differentiate from sibling tools like faf_guide or faf_auto, and 'Gold Code' is undefined, slightly reducing clarity.

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?

The description implies a two-step usage (first get questions, then apply answers) but does not explicitly state when to use this tool vs alternatives. No mention of prerequisites or when not to use it, leaving usage somewhat ambiguous.

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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