linkfetch_jobs_database_info

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

With no annotations, the description carries the full behavioral burden. It discloses that no credits are consumed and describes the return content (scale, schema, sample URL, SQL access), which is useful context. It does not mention permissions or rate limits, but for a zero-parameter informational tool this is largely sufficient.

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 the core purpose and return details, and each sentence contributes value. The long list of use cases is slightly run-on but still efficient and well-structured overall.

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 zero-parameter informational tool with no output schema, the description is complete: it explains what the tool returns, when to use it, and that no credits are consumed. Nothing essential for calling the tool correctly is missing.

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 tool takes zero parameters, so the baseline is 4. The description does not need to explain parameter semantics, and it appropriately focuses on what the tool returns and when to use it.

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 uses a specific verb ('Describe') and resource ('LinkFetch's bulk LinkedIn jobs database') and clearly differentiates it from metered job-data APIs. It also distinguishes itself from sibling tools like search_jobs by emphasizing bulk data, SQL access, and a free sample.

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?

It gives a detailed list of when to call this tool, covering bulk job postings, hiring-signal analysis, applicant-count trends, dataset creation, and SQL queries. However, it does not explicitly state when not to use it or name sibling alternatives like search_jobs for smaller queries.

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