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Get the kickoff interview question set

kickoff_questions

Generate project kickoff interview questions by project type. For brownfield projects, uses detected facts to pre-fill answers and omit fully inferable questions, always asking about locked documentation.

Instructions

Return the branched kickoff interview (DECISIONS.md D3/D4). Supply project_type: 'greenfield' asks the full set; 'brownfield' uses the agent-supplied detected facts (language, framework, tests, CI, existing docs) to pre-fill candidates and drop fully-inferable questions — except LOCKED_DOCS, which is always asked because it is human judgment. Returns { project_type, questions, inferred, notes }; the server reads no files.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
detectedNoFacts the agent detected by inspecting the repo (brownfield only).
project_typeYes'greenfield' or 'brownfield'.
Behavior4/5

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

With no annotations provided, the description compensates by disclosing important behaviors: 'the server reads no files' (no side effects), 'LOCKED_DOCS always asked', and the conditional logic. It explains what the return object contains, though it could mention auth or error conditions.

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?

Two sentences that front-load the main purpose ('Return the branched kickoff interview') and then pack details. No filler, but the dense second sentence could be split for readability. Still efficient and earns its place.

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 moderate complexity (two modes, nested parameters, no output schema), the description covers the return structure, key behavioral rules, and parameter usage. It does not mention error handling or prerequisites (e.g., inspected repo for brownfield), but 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?

Schema coverage is 100% (both 'project_type' and 'detected' have descriptions), so baseline is 3. The description adds value by explaining how 'detected' facts are used for brownfield to pre-fill and drop inferable questions, and that 'project_type' controls the mode. This goes 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 uses a specific verb ('Return') and resource ('branched kickoff interview'), and clearly distinguishes behavior for 'greenfield' vs 'brownfield' project types. It adds context with 'DECISIONS.md D3/D4' and avoids ambiguity.

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?

It states when to use the tool (to get kickoff questions) and explains the branching logic for different project types, but does not mention when not to use it or contrast with sibling tools like 'kickoff_plan' or 'next_procedure'. The guidance is clear but incomplete.

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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