analyze_job

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

Describes key behaviors: verifies profile existence, fetches job, checks visa, returns error envelope on failures, never raises exceptions. With no annotations, this is sufficient disclosure.

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?

Well-structured with numbered steps and return list. Somewhat lengthy but efficient; front-loaded with main 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?

Complete for a single-parameter tool: covers flow, error handling, and return fields. Output schema exists, so detailed return specs are unnecessary.

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?

Only one parameter, 'url', described as 'raw job posting URL'. Schema has no description, so the description adds needed clarity.

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 gathers job, visa, and profile data for scoring, distinguishing it from siblings by combining multiple fetches into one call.

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?

Explicitly states the tool is the data-gathering half, and after calling it, the AI should compute the score itself. It mentions error handling via return envelope. No explicit when-not-to-use, but clear context.

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