query_alphamissense

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

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It states the tool returns a class and score but does not detail error behavior (e.g., handling of invalid rsIDs or non-missense variants), rate limits, or authentication requirements. The transparency is minimal.

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 one sentence with no extraneous information. It efficiently conveys the purpose and output. A slightly more structured format (e.g., stating input, output) would merit a 5.

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 simple input schema (1 parameter) and no output schema, the description adequately covers what the tool does and what it returns (class and score). It lacks details like possible class values but is complete enough for an agent to understand basic usage.

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 schema already describes the single parameter 'rsid' as a dbSNP rsID, achieving 100% coverage. The description adds no new semantic meaning about the parameter beyond what is in the schema, so a baseline score of 3 is appropriate.

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 identifies the tool as performing a lookup of AlphaMissense AI pathogenicity predictions for missense variants, specifying it returns class and score. This distinctly separates it from sibling tools like query_cadd or query_clinvar which serve different variant annotation purposes.

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 implies usage when an AlphaMissense prediction is needed but lacks explicit guidance on when to use this tool versus alternatives (e.g., query_cadd, query_clinvar). No prerequisites or exclusion criteria are mentioned, leaving selection to the agent's inference.

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