detect_design

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

The annotations already declare readOnlyHint=true, so the read-only behavior is covered. The description adds that it 'Returns the guessed design plus the columns that drove the inference,' which is useful, but it does not disclose other behavioral traits such as performance, failure modes, or constraints beyond what the annotations and schema already provide. With annotations present, this is adequate but not rich.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences with no filler. It front-loads the core function, lists the design categories, states the return value, and gives the usage rule with a sibling reference. Every sentence earns its place.

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

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

Given the presence of a detailed input schema, an output schema, and annotations, the description is complete enough for an agent to decide when and how to invoke the tool. It states the trigger condition, the call ordering relative to recommend(), and what the tool returns, so no critical contextual information is missing.

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

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

Schema description coverage is 75%, so the schema carries most of the parameter meaning. The description adds little beyond implying column inspection ('from column shapes and types'), and it does not clarify the purpose of the undocumented id_col_hint or time_col_hint parameters. This matches the baseline for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Auto-detect'), a specific resource ('study design'), and the types of design detected ('panel / cross-section / RD / IV-style'). It also explicitly differentiates itself from recommend() by saying 'Call this BEFORE recommend()', which allows an agent to distinguish it from a key sibling without opening schemas.

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

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

The description gives a clear, explicit usage context: 'Call this BEFORE recommend() when the user pastes a CSV with no context.' This is strong guidance, but it does not mention when not to use the tool or name alternative detection/design-intake siblings such as design_intake. It provides clear context without full exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.