recommend_test

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

With no annotations provided, the description carries the full burden. It clearly states what the tool returns: the test, assumptions, SPSS path, R code, APA template, and fallbacks. This provides good transparency about the tool's output, though it does not mention non-side-effect traits like data handling or auth requirements, which are not critical for this tool.

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 concise at about 5 sentences, front-loading the core purpose, then listing required inputs and expected outputs. Every sentence adds value, with no redundant or unclear phrasing.

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 tool's complexity and lack of output schema, the description covers the essential aspects: input requirements and output contents. It provides enough detail for an agent to use it correctly. A minor improvement could be including an example of a full input, but it is still quite complete.

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 coverage is 0%, so the description must compensate. It explicitly explains all 5 parameters: outcome_type, design, n_groups, normality, and equal_variance, with examples and guidance. This adds significant meaning beyond the raw schema, which only lists enums without descriptions.

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 clearly states the tool's purpose: 'Recommend the correct statistical test for a study design.' It specifies the verb (recommend), resource (statistical test), and context (study design). This distinguishes it from siblings like check_assumptions or interpret_result, which focus on different aspects of statistical analysis.

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 explicitly says 'Use this to answer "what statistical test should I use?"' and guides the user on what information to provide (outcome type, design, etc.). However, it does not explicitly mention when not to use this tool or directly contrast with siblings, though the context makes the differentiation clear.

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