identify_cages

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

With no annotations, the description covers key behaviors: explains method options (TRACE vs HTR), input format (GROMACS .gro), and return data (cage counts, structure label, confidence). However, it does not mention potential performance constraints or post-processing steps.

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 (three sentences) and structured logically: purpose, output, method options, input format. No redundant information.

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?

The description lacks detail on most parameters and does not specify output format or structure. With no output schema, the agent needs more context on return data to use the tool effectively.

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. Only the method parameter is explained; path, rcut, frame, and theta are left undefined. This is insufficient for a 5-parameter tool with no schema 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 identifies the tool's purpose: identifying clathrate-hydrate cages and classifying crystal structure. It lists specific cage types (e.g., 5^12, 5^12 6^2) and output (counts, structure label, confidence), making it distinct from siblings like hbond_network or render_cages.

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?

No guidance on when to use this tool versus alternatives. Siblings like hbond_network or measure_geometry could be confused, but the description does not clarify selection criteria or exclusions.

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