LinkedIn Custom MCP Server

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

Annotations only provide a title, so the description carries the full burden of behavioral disclosure. It indicates this is a creation tool (implying mutation) and specifies visibility options, but does not cover other behavioral traits like authentication needs, rate limits, or what happens on success/failure. The description adds some context (visibility settings) but is incomplete for a mutation tool with no annotations.

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 in the first sentence, followed by parameter details. It is appropriately sized with no redundant information, though the parameter explanations could be slightly more integrated into the flow rather than listed separately.

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 (which handles return values), the description focuses on input semantics and purpose. It covers the basic action and parameters adequately for a creation tool, but lacks details on behavioral aspects like error handling or side effects, which would be beneficial given the mutation nature and sparse annotations.

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%, so the description must compensate. It adds meaningful semantics by explaining 'text' as 'The content of the post' and 'visibility' with enum values 'PUBLIC' or 'CONNECTIONS', which clarifies beyond the schema's basic types. However, it does not detail constraints like text length or default behavior for visibility, leaving some gaps.

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 specific action ('Create a new text-based update') and resource ('on the user LinkedIn feed'), distinguishing it from siblings like linkedin_create_image_post (which handles images) and linkedin_update_post (which modifies existing posts). The verb 'create' and resource 'feed post' are precise 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 for text-based posts on LinkedIn feeds but does not explicitly state when to use this tool versus alternatives like linkedin_create_image_post or linkedin_update_post. It provides basic context (text-based updates) but lacks explicit guidance on exclusions or prerequisites, such as when image posts are more appropriate.

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