tool_recommend_projects

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

No annotations are provided, so the description carries the full behavioral burden. It discloses that it searches GitHub and uses an LLM to generate learning paths and resume STAR examples, but it omits operational details such as authentication needs, rate limits, latency, or cost.

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 compact, front-loads the core purpose, and uses an Args section to document parameters efficiently. Every part earns its place, though the title is absent and the parameter list could be slightly more structured.

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, the description need not explain return values. For a three-parameter recommendation tool with no annotations and zero schema coverage, it adequately covers purpose and parameters, but it lacks usage alternatives and deeper behavioral context.

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 documents all three parameters with useful meaning: skills are skill keywords with examples, language is the preferred primary language with default python, and provider is an optional LLM provider.

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 verb and resource: retrieves GitHub high-value open-source projects for skill gaps, then has an LLM generate a crash learning path and resume STAR examples. The purpose is clear, but it does not explicitly distinguish itself from sibling tools such as resume or JD analysis tools.

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 phrase '针对技能短板' gives an implied when-to-use context, so the agent can infer this is for skill-gap project discovery. However, it does not state when not to use it or compare it to alternatives among the sibling tools.

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