Review Research Idea

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

With no annotations, the description discloses that scoring is heuristic and based on keyword overlap, not language-model judgment, which gives transparency into the tool's behavior. However, it does not explain the output format or any side effects.

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, well-structured sentence that conveys the action, scoring dimensions, and nature of the tool without any filler or redundancy.

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 three parameters and complex paper object, the description omits guidance on required paper fields and does not describe the output. This lack of completeness forces the agent to guess, especially for a tool with no output schema and no annotations.

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 adds no meaning beyond parameter names. The complex 'selected_papers' object with many optional fields lacks guidance on which fields are important, leaving the agent without sufficient context.

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 reviews a research idea against selected papers, scoring novelty, feasibility, and literature support. It distinguishes from siblings by specifying the heuristic, keyword-based approach and that it is not a language-model judgment.

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 when a user has a research idea and specific papers to evaluate. It notes scores are relative heuristics, hinting at limitations, but does not explicitly state when not to use or suggest alternatives.

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