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

The description goes beyond the schema and annotations by detailing operational behaviors: roles are sourced from career boards and are 're-checked daily.' It also explains complex field relationships, such as the difference between `url` and `apply_url`.

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 definition is detailed but highly efficient. It is structured logically, moving from broad purpose to specific data nuances and ending with parameter explanations, ensuring every sentence adds value.

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?

The description is exceptionally complete. It explains not just what the tool does, but where the data comes from, how often it is updated, and the practical implications of the output fields, making it highly robust for an AI agent.

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?

Given the low schema coverage (33%), the description is crucial. It provides valuable usage advice for critical parameters, such as defining `query` as 'free text like the site's search box' and explaining how `max_years` functions to filter out roles requiring more experience.

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 tool is clearly defined as a search function specifically for software engineering roles on engg.space. It clearly distinguishes itself as a list-retrieval mechanism compared to the likely single-item retrieval sibling, get_job.

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 excellent context on its coverage (funded startups, AI labs) and the nature of the data sources (career boards). It provides strong environmental usage guidelines, even if it doesn't explicitly state 'use this for searching vs this other action.'

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