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

With no annotations provided, the description carries the full burden and does substantial work: it discloses defaults (kind empty means first two types, org_nature default includes banks, only_open defaults to true), coverage quirks (incidental private/unknown tail), and a no-guessing policy with a recommendation to verify source_url for major decisions. It also lists important return fields such as deadline status and data review date.

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 front-loaded with a one-line purpose, then uses a tight bullet-style parameter list, followed by return-field and caveat notes. Despite the large number of parameters, every line adds a distinct piece of operational information. The length is justified and easy to scan.

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?

For a tool with 10 optional parameters, no annotations, and no schema descriptions, the description covers all operational needs: filter semantics, defaults, allowed values, pagination, return fields, and the null/no-guess caveat. An agent can select and invoke the tool correctly without supplementary documentation, and an output schema is also present for return shape.

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?

The input schema has 0% description coverage, but the description fully documents all 10 parameters with concrete matching semantics, allowed values for kind and org_nature, defaults, pagination behavior, and example values like '深圳' and '2027'. The description fully compensates for the schema's lack of parameter documentation.

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 opening line states a specific verb and resource: condition-based search for state-owned campus recruitment positions and projects. The enumerated filter dimensions (city, major, graduate year, kind, org nature) make the scope concrete and clearly distinguish it from sibling tools focused on details, stats, deadlines, or recent changes.

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 implies usage by showing how to filter jobs/projects and documents defaults, but it never explicitly states when not to use this tool or when to prefer a sibling such as get_job_detail or list_deadlines. An agent can infer the primary use case, but exclusion/alternative guidance is left to the agent's judgment.

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