research_similar

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

Beyond the readOnlyHint annotation, the description discloses meaningful behavior: mode semantics ('similar', 'citers', 'references'), fallback to 'similar' for unknown values, min_score as a renderer-side relevance floor, k's low-score trimming, and rerank being omitted when None because the API default is undocumented. It even notes the unexposed anchor parameter, giving the agent a fuller mental model.

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 structure is front-loaded with the core purpose, followed by scope and a well-organized Args block. Every sentence adds value, including the note about anchor not being exposed, which prevents an agent from expecting an undocumented parameter. Despite the length, it is dense with necessary information rather than padded.

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?

All six parameters are documented, the scope is clarified, a sibling alternative is provided, and the return shape is referenced as 'same shape as research_papers' with an output schema available. The description leaves no material gap for an agent to correctly select and invoke the tool.

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 fully compensates by explaining every parameter: paper_id accepts a paperId or 'arxiv:...' string, intent is a required natural-language description with examples, k has a range/default/API maximum, mode has enumerated meanings, min_score explains how it interacts with k, and rerank clarifies optionality and omission behavior. No parameter is left to guesswork.

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 opens with a specific action and resource: 'Expand from a seed paper to related work via the Research Index.' It also distinguishes itself from siblings by noting it returns the same shape as research_papers and is arXiv-scoped, with a clear pointer to web_search for non-AI/ML literature. This is not a tautology and lets an agent understand exactly what the tool 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 explicitly states when this tool applies: arXiv-scoped like research_papers, and it gives a concrete alternative: 'for non-AI/ML literature use web_search(category="publication").' It also implicitly distinguishes from research_paper/research_github by framing this tool as expansion from a seed paper to related work.

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