find_professors

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

With no annotations, the description fully bears the burden. It details the pipeline steps: search, concurrent multi-source detail fetching, relevance ranking, and Markdown table rendering. It also explains keyword weighting and scoring. It doesn't cover rate limits or auth, but overall it's 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 long but well-structured with a clear summary, a highlighted important section, and a bulleted args list. It front-loads the purpose and key instructions. Minor redundancy could be trimmed, but it's well-organized.

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 tool has 12 parameters and no output schema, the description is very complete. It explains the full pipeline, return value structure (including keys like markdown, ranked_professors, total), and important constraints like concurrent fetching and auto-analysis fallback.

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%, but the description extensively explains every parameter, especially the most critical ones (topic_keywords, domain_keywords, weights). It provides usage examples and clarifies the role of each parameter, far beyond the schema property titles.

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 this is a 'One-shot professor finder (RECOMMENDED entry point)' and explains it runs a full pipeline including search, concurrent fetch, ranking, and Markdown rendering. It distinguishes itself from siblings like search_professors by being the comprehensive entry point.

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 provides excellent guidance on how to split keywords into topic vs domain for precise results, including examples. It also explains fallback behavior if only keywords are given. While it doesn't explicitly say when not to use it or directly compare to siblings, the guidance is clear and actionable.

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