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

No annotations are provided, so the description carries the full burden. It discloses that the tool suggests directions based on inputs, which is sufficient for a recommendation tool. It does not mention limitations, but the behavior is transparent for the intended use.

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?

Two concise sentences that front-load the purpose and input methods. No extraneous 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 details about the output format or depth of suggestions. Given the complexity (5 parameters, two modes) and no output schema, more context on return value would improve completeness.

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 only 20%, but parameter names are self-explanatory (interests, strengths, favoriteSubjects). The description explains the two modes (test_result and conversational) but does not detail individual parameters beyond that. This is adequate but not compensating fully for low coverage.

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: suggesting suitable majors/careers based on interests and strengths. It distinguishes between two input modes (test result code or conversational input), setting it apart from sibling tools like explore_job or get_major.

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 specifies input types (test code or conversational), giving clear context on when to use. It does not explicitly mention when not to use or alternatives, but sibling tool names provide implicit guidance.

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