Expand Selection

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

With no annotations, the description carries the transparency burden. It thoroughly lists the return payload (full details, graph neighbors, counterbalances, uncovered categories), making clear it's a read/expansion operation. It doesn't explicitly state that no state is modified, but the language strongly implies it's informational.

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 and well-organized. The opening sentence states the core purpose, followed by a bulleted list of return types. Every sentence serves a purpose, and the structure aids scanning.

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 tool with two straightforward parameters, no annotations, and no output schema, the description is complete. It explains what the tool returns (via bullets) and where it fits in the workflow, providing sufficient context for the agent to invoke it correctly.

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 100% for the two parameters, and both are described adequately in the schema. The description adds only contextual reinforcement ('after start_analysis' for sessionId, 'models you've chosen' for modelIds), not new parameter-level semantics, 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?

The description clearly states the tool's function: 'Get full details for selected models plus their graph neighbors and counterbalances.' It uses a specific verb ('get') and resource ('selected models plus graph neighbors'), and distinguishes itself from siblings by positioning it between start_analysis and apply_lens.

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?

Explicit workflow guidance is given: 'Call after start_analysis with the model IDs you've chosen' and 'Use this to review your selection before committing to apply_lens calls.' This unambiguously tells when to use the tool relative to alternatives.

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