LinkedIn Intelligence MCP Server

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

With no annotations provided, the description carries full burden. It discloses that view count requires Partner API access (a behavioral constraint), and mentions the return format includes engagement metrics. However, it doesn't cover rate limits, authentication requirements, error conditions, or whether this is a read-only operation (though 'get' implies it).

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 efficiently structured with a clear purpose statement, parameter documentation, return information, and an important note. Every sentence adds value with zero wasted words. The information is front-loaded with the core purpose first.

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 has an output schema (so return values don't need explanation in description), one simple parameter, and no annotations, the description provides good context. It covers purpose, parameter meaning, return content, and a key access limitation. For a straightforward read operation, this is reasonably complete, though could mention authentication or rate limits.

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 description adds meaningful context for the single parameter: 'post_urn: LinkedIn post URN' clarifies what the parameter represents beyond the schema's basic string type. With 0% schema description coverage and only one parameter, this provides adequate semantic information, though it doesn't explain URN format or validation rules.

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's purpose: 'Get analytics for a specific post' with a specific resource (LinkedIn post) and verb (get analytics). It distinguishes from siblings like 'get_my_post_analytics' by specifying 'for a specific post' rather than 'my posts', though it doesn't explicitly name the sibling alternative.

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 context through the note about Partner API access for view count, but doesn't provide explicit guidance on when to use this tool versus alternatives like 'get_my_post_analytics' or 'analyze_content_performance'. No when-not-to-use guidance or prerequisites are mentioned.

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