LinkedIn Intelligence MCP Server

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

No annotations are provided, so the description carries the full burden. It states the action ('publish') and return ('published post details'), but lacks critical behavioral details: permission requirements, whether publishing is irreversible, rate limits, error conditions, or how visibility affects the post. For a mutation tool with zero annotation coverage, this is insufficient.

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 well-structured and front-loaded: the first sentence states the purpose, followed by clear 'Args' and 'Returns' sections. Every sentence adds value without redundancy, making it efficient and easy to parse.

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 (a mutation with 2 parameters), no annotations, and an output schema (which handles return details), the description is moderately complete. It covers purpose and parameters but lacks behavioral context (e.g., side effects, errors). The output schema reduces the need to explain returns, but more guidance on usage and transparency would improve completeness.

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 meaning by explaining 'draft_id' as 'ID of the draft to publish' and 'visibility' as 'Post visibility - PUBLIC, CONNECTIONS, or LOGGED_IN', including enum values not in the schema. This clarifies both parameters beyond their schema definitions, though it doesn't cover default behavior for 'visibility'.

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: 'Publish a draft as a LinkedIn post.' It specifies the verb ('publish') and resource ('draft'), but doesn't explicitly differentiate from sibling tools like 'create_post' or 'schedule_post', which also create posts. The distinction is implied (publishing vs. creating/scheduling), but not explicitly stated.

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 doesn't mention prerequisites (e.g., needing a draft from 'create_draft'), exclusions, or comparisons to tools like 'create_post' (for direct posting) or 'schedule_post' (for delayed posting). Usage is implied but not articulated.

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