get_job_details

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

With no annotations provided, the description carries the burden of behavioral disclosure. It does state the input is a Naukri URL and the output is the 'full description,' which conveys a read-only operation. However, it doesn't mention network effects (e.g., requires internet, might fail for invalid URLs), whether it requires an active session (given the sibling naukri_login and naukri_session_status tools), or whether output is structured. The description is adequate but leaves behavioral questions about session requirements and error handling unanswered.

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?

One sentence, fourteen words, zero waste. The key scoping constraint 'from its Naukri URL' is prominently placed. Every word serves the semantic goal of explaining what the parameter expects and what the tool returns.

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 simple single-parameter retrieval tool with clear output implied by 'full description,' the description is nearly adequate. However, it doesn't address the session-dependency question—the sibling set includes naukri_login/session_status/close_session, suggesting authentication state might affect behavior, and this tool doesn't clarify whether a session is required. It also doesn't explicitly state the return is in a specific format (JSON vs. HTML content). The description is complete for the happy path but leaves the agent guessing on auth requirements and error/expired-session scenarios.

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%, meaning the schema provides no description for the job_url parameter. The description's phrase 'from its Naukri URL' provides crucial semantic context: the parameter expects a Naukri job URL specifically, not just any URL. Is that enough to reach 4? For a single string parameter, the description does add meaning beyond the raw schema. However, it could be more explicit about the URL format (e.g., 'e.g., https://www.naukri.com/job/...'). A 3 is fair because while it provides the best possible guidance for the one parameter, it doesn't specify expected format, validations, or examples.

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 gets the full description of a single job, specifying the resource (job) and source (Naukri URL). It distinguishes from siblings like search_jobs (which searches) and get_saved_jobs (which lists), though it could more explicitly name these alternatives. The verb 'Get' and object 'full description of a single job' are specific and unambiguous.

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—when you have a Naukri URL and need its full details—but doesn't explicitly state when not to use it or name alternatives like search_jobs or get_recommended_jobs for discovery. An agent could infer 'use when you have a job URL,' but the tool would be stronger with an explicit 'use search_jobs to find jobs by query' or similar guidance. There is no explicit exclusion of cases requiring a different tool.

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