research_topic

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

With no annotations, the description must disclose all behavioral traits. It only mentions two optional behaviors (Sci-Hub fallback, graph rendering) but fails to describe the core workflow steps, sources searched, or output format, leaving significant ambiguity.

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?

Three sentences, concise and front-loaded with the main purpose. No wasted words, though a bit more structure (e.g., listing key features) would improve 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 tool's complexity (18 parameters, no output schema, no annotations), the description is incomplete. It omits details on the overall workflow, expected outputs, and default behavior, leaving the agent with many unknowns.

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 must compensate. It only explains two parameters (include_scihub, write_graph) out of 18, leaving most parameters like topic, limits, and year ranges undocumented. Parameter names are somewhat self-explanatory but insufficient.

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 runs an end-to-end research workflow with optional Zotero sync, which is a specific verb and resource. However, it does not differentiate from sibling tools like deep_read_topic or graph_topic, so it misses explicit distinction.

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 some context by mentioning optional flags (include_scihub, write_graph) but gives no explicit guidance on when to use this tool versus alternatives. It lacks when-not to use or prerequisites.

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