get_linkedin_company_employees

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

With no annotations provided, the description carries full burden but offers minimal behavioral insight. 'Get employees' implies a read operation, but it doesn't disclose rate limits, authentication requirements, data freshness, pagination behavior, or what happens when companies parameter contains invalid URNs. For a tool with 6 parameters and no output schema, this leaves significant behavioral questions 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?

The description is a single, efficient sentence that gets straight to the point with zero wasted words. It's appropriately sized for a tool with clear naming and good schema documentation, though its brevity contributes to gaps in other dimensions.

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?

Given the tool's complexity (6 parameters, no annotations, no output schema), the description is inadequate. It doesn't explain what the tool returns (employee profiles? basic info?), how results are structured, whether all parameters work together, or any limitations. For a data retrieval tool with multiple filtering options and no output schema, users need more context about what to expect.

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 100%, so all parameters are documented in the schema itself. The description adds no additional parameter semantics beyond implying the tool operates on LinkedIn company data. It doesn't explain relationships between parameters (e.g., how keywords interacts with first_name/last_name) or provide context about the companies parameter beyond what's in the schema. Baseline 3 is appropriate when schema does the heavy lifting.

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 verb ('Get') and resource ('employees of a LinkedIn company'), making the purpose immediately understandable. It distinguishes this tool from siblings like get_linkedin_company (which gets company info) or get_linkedin_user_connections (which gets personal connections). However, it doesn't specify whether this retrieves current employees, all employees, or includes filtering capabilities beyond what the parameters imply.

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. With siblings like search_linkedin_users (which might search across LinkedIn) and get_linkedin_user_connections (which gets connections for a specific user), there's no indication whether this tool is for company-specific employee lookup, how it differs from broader searches, or any prerequisites for accessing company employee data.

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