rank_fit

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

The description discloses deterministic whole-word keyword matching and separates server-side and client-side responsibilities. However, it lacks details on potential limitations, error handling, or behaviors like what happens with no matches. Without annotations, more transparency would be beneficial.

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 somewhat lengthy but well-structured with sections for server-side, client-side, args, and returns. Each sentence serves a purpose, though some repetition could be trimmed. It balances detail with readability.

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 complexity (4 parameters, nested objects, no output schema, no annotations), the description is quite complete. It covers input formats, output structure, and usage flow. Missing some edge cases like default filters behavior, but overall adequate.

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 description coverage is 0%, so the description carries the burden. It explains user_interests format in detail (including optional fields like preset, description, paper_urls, weighting) and briefly mentions filters and sort_by options. This adds significant value beyond the schema's generic 'object' type.

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 ranks professors by keyword overlap and packages materials for client LLM fit judgment. The verb 'rank' and resource 'professors by keyword overlap' is specific and distinguishes it from sibling tools like find_professors or search_professors.

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 explains that the input professors should come from search_professors or get_professor_details, and instructs the client to use fit_materials for judgment. It does not explicitly mention when not to use this tool, but provides clear context for its use.

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