linkedin_add_education

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

The description adds one useful behavioral detail: 'School names are matched against LinkedIn's directory.' This goes beyond annotations by indicating potential validation or normalization behavior. However, it does not disclose other traits like duplicate handling, response format, or error behavior. Annotations already cover the read-only/idempotent/destructive hints, so this is acceptable but not rich.

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?

Two sentences, no fluff. The first sentence delivers the core purpose; the second adds a relevant constraint. Front-loaded and appropriately sized for a simple add operation.

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 9-parameter tool with no output schema, the description is minimal. It leaves open questions: what happens when a school doesn't match the directory? Does it fail or create an unrecognized entry? It also doesn't clarify whether all other parameters are optional (though the schema does). It is adequate for simple use but lacks enough context to anticipate real-world edge cases.

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 coverage is 78%, so most parameters are already described in the schema. The description adds meaning only to the 'school' parameter by noting directory matching. Other parameters (grade, degree, dates, etc.) rely entirely on the schema. This is adequate but does not fully compensate for the remaining 22% of unannotated params.

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 states a specific verb+resource: 'Add an education entry to the profile.' This clearly distinguishes it from sibling tools like linkedin_add_experience or linkedin_add_skill by naming the resource type ('education entry'). It is direct 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 provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, exclusions, or suggest alternatives for other profile sections. It is a bare statement of function without usage context.

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