gene_phenotype_detail

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, setting a baseline for safety. The description adds context beyond annotations: it explains why this is a tool (SQL double-counting) and describes the verdict types. This is helpful but not exhaustive—there is no mention of rate limits, authentication needs, or what happens if inputs are invalid. The description adds some behavioral insight but falls short of full transparency.

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 verbose and includes extraneous technical reasoning about SQL double-counting and the tool's origin. While informative, the first three sentences could be condensed. The key actionable information (handling lists, verdict meanings) appears only in the last two sentences. The structure lacks front-loading of essential details, making it less efficient for an AI agent.

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 output schema exists (context confirms), the description does not need to detail return values. It explains the tool's purpose, input batch behavior, and verdict semantics, which suffices for most use cases. However, it omits specifics about supported biobanks, ancestry strata, or example phenotype IDs. The description is adequate for an expert but could be more complete for general AI consumption.

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?

The input schema covers all 5 parameters with descriptions (100% coverage), so the baseline is 3. The description adds extra value: it notes that the 'gene' parameter accepts a comma-separated list and that using a list returns a verdict per gene. It also clarifies the default for 'maf' and 'mask'. This goes beyond the schema, improving parameter understanding for batch usage.

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 opens with a clear question defining the tool's purpose: 'Does a gene-trait association replicate across ancestries and biobanks?' This directly states the function. It also distinguishes itself as a specialized view that handles a SQL double-counting issue, which helps set it apart from generic query tools like the sibling 'query'. However, the purpose is embedded within technical reasoning, slightly reducing clarity for a general AI agent.

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 provides some usage advice: passing a comma-separated list to screen multiple genes, noting that one gene at a time costs a call each. It also explains the verdict distinction between 'underpowered' and 'discordant'. However, it does not explicitly say when to use this tool versus its siblings ('query', 'schema', 'variants') or when not to use it. The guidance is present but implicit and incomplete.

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