analyze_repo_for_resume

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

No annotations are provided, so the description carries the transparency burden. It discloses the key behavioral constraint (不编造量化指标 - does not fabricate quantitative metrics), which is valuable honesty about what the tool will NOT do. However, it doesn't disclose output structure, error behavior, or what happens when repo_info is incomplete, leaving some gaps for a tool with no annotation support.

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 reasonably succinct with a clear header line followed by a brief 'when to call' section. The structure with the explicit 什么时候该调用 (when to call) header makes the pipeline context scannable. Minor waste exists in restating thresholds in the usage section, but overall it's efficient and front-loaded with 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?

With an output schema present and a well-defined pipeline position, the description covers the key decision context. It explains the github_username contribution logic, the ordering relative to siblings, and the honesty constraint. It's not exhaustive (doesn't explain repo_info handling or fallbacks), but given the output schema exists and the tool has a clear niche in a documented workflow, it's adequately complete.

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 must compensate. It explains the github_username parameter's role (judging contribution_level with thresholds) but doesn't describe repo_info or target_role beyond what the schema trivially shows. The contribution_level thresholds (primary ≥50% / contributor 20–50% / minor <20%) are genuinely useful semantics not in the schema, though two of three params remain unexplained.

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 states a specific purpose ('从仓库客观数据提炼可用于简历的技术要点' - extract resume technical points from repo data) with a clear constraint (不编造量化指标 - don't fabricate metrics). It distinguishes somewhat from siblings by its position in the pipeline (between fetch_repo_info and generate_bullet_points), though it doesn't deeply contrast with generate_bullet_points which could overlap in purpose.

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 explicitly states when to call it ('fetch_repo_info 成功之后、generate_bullet_points 之前' - after fetch_repo_info succeeds, before generate_bullet_points). It also explains usage of the github_username parameter to determine contribution level, providing clear pipeline ordering guidance that differentiates it from sibling tools.

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