LinkedIn Intelligence MCP Server

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes critical behavioral traits: the tool uses an unofficial API (implying potential instability), may trigger bot detection, and could result in account restrictions if overused. It also mentions the return value ('success status and message details'), though it doesn't detail error conditions or rate limits.

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 with a clear purpose statement, parameter explanations, return value note, and a separate WARNING section. Every sentence adds value, but the warning could be slightly more concise (e.g., combining the last two sentences). It's appropriately sized for a tool with significant behavioral implications.

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 (mutating action with high-risk behavior), no annotations, 0% schema coverage, but an output schema present, the description does a good job covering purpose, parameters, returns, and critical warnings. It adequately informs the agent about risks and usage, though it could benefit from more specifics on error handling 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?

Schema description coverage is 0%, so the description must compensate. It clearly explains both parameters: 'recipients' as 'List of LinkedIn profile public IDs' with examples, and 'text' as 'Message content to send.' This adds essential semantic meaning beyond the bare schema types, though it doesn't specify format constraints like message length limits.

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 ('Send a LinkedIn message') and target resource ('to one or more recipients'), distinguishing it from sibling tools like 'send_connection_request' or 'create_comment' that involve different LinkedIn interactions. It provides a complete verb+resource+scope statement without being tautological.

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 includes a WARNING section that provides clear context about when to use this tool cautiously (due to unofficial API risks and potential account restrictions), but it doesn't explicitly compare it to alternative messaging methods or specify exact thresholds for 'too many messages.' It offers responsible usage guidance without naming specific alternatives.

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