paper_ai_analyze

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

With no annotations provided, the description carries the full burden. It successfully discloses critical behavioral traits: external API dependency (via env vars), the conditional PDF download attempt with abstract fallback, and JSON output format. However, it omits other behavioral details like timeout behavior, API cost implications, or caching policies.

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 well-structured with clear sections (functionality, environment setup, fallback logic, Args/Returns). The Args/Returns sections are necessary given the schema's lack of descriptions, though they echo structural fields. Every sentence conveys essential information without 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 has an output schema (covering return structure) and only one input parameter, the description is sufficiently complete for its complexity level. It covers the essential external dependency setup and conditional behavior (PDF fallback) needed for an agent to understand operational requirements.

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?

With 0% schema description coverage, the description fully compensates by documenting the single parameter: it explains 'doi' represents the paper's DOI and provides a concrete example ('10.1109/tim.2021.3106677'), which aids the LLM in generating correct invocations.

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 uses AI to analyze papers and specific outputs expected (core contributions, methods, findings). It implicitly distinguishes from siblings like `paper_download` (which merely fetches files) and `paper_search` (which finds papers) by emphasizing AI-driven content analysis, though it could explicitly contrast with these alternatives.

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 implicit guidance by noting it requires OpenAI-compatible API environment variables and explaining the fallback behavior (PDF full-text vs abstract). However, it lacks explicit guidance on when to choose this over `paper_download` or whether users should prefer manual download for large batches.

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