find_topic_researchers

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses the ranking method (by matching paper count, then citations within the returned sample) and the limitation of being a discovery aid, not exhaustive. However, it does not mention the return format or any potential edge cases (e.g., empty results), though an output schema exists. It provides meaningful behavioral context but not complete coverage.

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-structured. The first sentence states the core purpose, followed by ranking and limitation notes, and then a clean Args list. It is front-loaded with the essential information and contains no fluff or repetition, maximizing readability for an agent.

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 that an output schema exists, the description need not explain return values. It covers the tool's purpose, ranking behavior, limitations, and parameter semantics. While it doesn't mention prerequisites or error scenarios, these are not critical for a straightforward search tool with an output schema. Overall, it provides sufficient context for correct invocation.

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?

The 'Args' section adds semantic value beyond the bare schema properties. It clarifies that 'topic' can be keywords, 'institution_id' is specifically an OpenAlex ID, and 'limit' is a number from 1 to 25 (a range not present in the schema). This significantly helps an agent understand parameter usage, making it more than a repetition of schema names.

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 states a specific verb ('Identify'), a resource ('researchers'), a context ('connected to a topic at one university'), and a method ('using matching works'). This clearly distinguishes it from siblings like search_researchers (general researcher search) and search_universities (institution search), leaving no ambiguity about what it does.

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 implies usage context by clarifying it is scoped to a single institution and is a 'discovery aid, not an exhaustive faculty directory or quality ranking.' However, it doesn't explicitly state when to prefer this tool over alternatives like search_researchers or when not to use it. The context is clear but exclusions are left to inference.

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