mgnify_list_runs

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

No annotations are provided, so the description carries the burden of conveying behavior. The verb 'List' implies a read-only operation, but no explicit statement about side effects, pagination behavior, or error handling is included. This is adequate but not richly transparent.

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 compact: two sentences, with the core action first, then filter details, then alternative routing. No redundancy or unnecessary detail.

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?

For a simple listing tool with a rich output schema, the description is complete. It states what the tool returns, how to filter, and when to use sibling tools instead, providing sufficient context for correct selection and invocation.

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 already documents page, size, and has_experiment_type with descriptions and an enum. The description only restates the enum values, adding little beyond the schema, so baseline 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?

Description opens with 'List sequencing runs', a clear verb and resource. It also distinguishes itself from sibling tools by explicitly directing study- or sample-filtered queries to mgnify_study_analyses or mgnify_sample_runs.

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?

Provides explicit filter guidance with the accepted has_experiment_type values. Also gives clear when-not-to-use guidance by naming the alternative tools for study/sample-scoped run listing.

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