paper_recommend

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

With no annotations provided, the description carries full behavioral disclosure burden. It successfully explains the internal pipeline (file scanning → feature extraction → keyword mapping → search) and output structure (JSON with detected libraries/terms, queries, paper list). It lacks explicit read-only confirmation or side-effect warnings, though 'analyze' and 'scan' imply non-destructive 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?

Excellent structure with three distinct sections: main purpose (2 sentences), Args documentation, and Returns documentation. No redundancy; every sentence contributes to understanding the tool's function, parameters, or output format.

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 (multi-stage feature extraction across file types) and 0% schema coverage, the description provides sufficient context: it explains the extraction targets (import libraries, algorithm terms, LaTeX titles), documents both parameters, and summarizes the output JSON structure without needing to repeat the full output schema.

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 fully compensates. The Args section documents workspace_path with a concrete example ('E:/半导体实验') and top_n with its default value (8), providing complete semantic meaning for both parameters that the schema lacks.

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 '分析工作区代码，自动推荐相关学术论文' (analyzes workspace code, automatically recommends academic papers) with specific mechanism details (scanning .py/.tex/.md, extracting imports/terms). It distinguishes from siblings like paper_search (manual query) and paper_download (specific paper retrieval) by emphasizing the code-driven feature extraction workflow.

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 implicit usage context is clear from the detailed mechanism description—use this when you have a codebase to analyze for paper recommendations. However, it lacks explicit when-not-to-use guidance or direct comparison statements like 'use paper_search instead for manual keyword queries'.

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