recommend_semantic_scholar_papers_for_paper

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

With no annotations, the description carries the full burden for behavioral disclosure. It only states the core function and gives no insight into how similarity is computed, whether any modifications occur, or what the response structure entails beyond the output schema. Minimal indication that this is a read-only operation.

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 a single, clear sentence that is front-loaded and free of filler. However, it is so minimal that it borders on under-specification, though it does effectively communicate the core purpose.

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?

For a tool with four parameters, no annotations, and an output schema, the description is insufficient. It omits key context about how parameters like pool_from and fields affect behavior, and does not explain the recommendation criteria or any preconditions.

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%, and the description does not explain any of the four parameters. 'paper_id' is implicitly linked to 'one positive example paper', but limit, fields, and pool_from are entirely unexplained, leaving the agent without necessary parameter meaning.

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 recommends papers similar to one positive example paper, using a specific verb ('Recommend') and resource ('papers'), and distinguishes itself from the sibling tool 'recommend_semantic_scholar_papers' by specifying 'one positive example paper'.

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?

No guidance is provided regarding when to use this tool versus alternatives like search or the batch recommendation tool. The intended usage is only implied by the phrase 'similar to one positive example paper', with no explicit context or exclusions.

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