LinkedIn 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 of behavioral disclosure. It states the tool 'share[s] a LinkedIn post' which implies a write/mutation operation, but doesn't clarify permissions needed, whether sharing is public/private, rate limits, or what happens upon success/failure. For a mutation tool with zero annotation coverage, this leaves critical behavioral traits unspecified.

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 front-loads the core purpose ('Share a LinkedIn post') and adds necessary detail ('with optional commentary'). Every word earns its place, with no redundancy or fluff, making it easy for an agent to parse quickly.

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 moderate complexity (a mutation with 2 parameters), lack of annotations, and no output schema, the description is minimally adequate. It covers the basic action but omits behavioral context, error handling, and output expectations. The schema handles parameters well, but overall completeness is limited by missing mutation-specific guidance.

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%, with clear documentation for both parameters ('postId' and 'commentary'). The description adds minimal value beyond the schema, only noting that commentary is 'optional'—which is already implied by the schema's lack of 'commentary' in required fields. Baseline 3 is appropriate as the 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 action ('share') and resource ('LinkedIn post'), making the purpose immediately understandable. It distinguishes itself from siblings like 'create_post' (new content) and 'comment_on_post' (adding comments to existing posts). However, it doesn't explicitly differentiate from 'like_post' or other engagement tools beyond the specific 'share' action.

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., authentication status), compare with similar tools like 'comment_on_post' or 'like_post', or specify scenarios where sharing is appropriate versus other actions. The agent must infer usage from context alone.

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