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

No annotations are provided, so the description carries the full burden. It discloses that the tool returns passages, citations, equations, and table cells, which implies a retrieval operation. However, it does not explicitly state side effects (e.g., read-only), limitations, or error behavior.

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 within two sentences. It directly states the purpose, gives query syntax, and lists return content without unnecessary filler.

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?

The description covers the core function, query syntax, and return content, but lacks details about output format, result structure, or edge cases. Given the absence of an output schema and parameter descriptions, more context would be helpful for an agent to fully understand expected results.

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?

Both parameters have empty descriptions in the schema, so the description must compensate. It indirectly explains 'query' by describing keyword syntax, but it does not explicitly define the parameter or the 'limit' parameter beyond its default value. This leaves significant ambiguity.

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 action ('Search original papers') and the subject domain ('agentic reinforcement learning, credit reinforcement learning and CLI agents'). It does not explicitly distinguish itself from the sibling tool 'search_tasks', which could cause ambiguity, but the focus on 'original papers' provides some differentiation.

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 explicit usage instructions for query syntax ('Use English keywords (AND), OR and quoted phrases') and describes the expected return content. It does not clarify when to use this tool versus alternatives like 'search_tasks', but the operational guidance is clear.

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