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. It discloses key behavioral traits: it returns data including view count and viewer information, notes limitations based on LinkedIn subscription, and includes a critical WARNING about using an unofficial API (implying potential risks like rate limits or instability). This adds significant value beyond basic functionality, though it could mention authentication requirements or data freshness.

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, followed by return details and a critical warning. Every sentence earns its place: the first states what it does, the second clarifies output, and the third provides essential risk disclosure. It's appropriately sized with zero waste.

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 (read-only data retrieval), no annotations, 0 parameters, and an output schema exists, the description is largely complete. It covers purpose, output semantics, and a key behavioral warning. However, it could briefly mention what the output schema contains (e.g., data structure) for full context, but the output schema likely handles that.

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 input schema has 0 parameters with 100% coverage, so the baseline is 4. The description doesn't add parameter details (as there are none), but it doesn't need to—it efficiently focuses on output semantics and warnings. No points are deducted since it compensates appropriately for the lack of parameters.

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 profile view statistics for the authenticated user.' It specifies the verb ('Get') and resource ('profile view statistics'), and distinguishes it from siblings like 'get_my_profile' or 'get_profile' by focusing on view metrics. However, it doesn't explicitly differentiate from analytics siblings (e.g., 'analyze_engagement'), so it's not a perfect 5.

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: it's for the authenticated user's profile views, and the WARNING about the unofficial API suggests caution. However, it doesn't explicitly state when to use this tool versus alternatives (e.g., 'get_my_post_analytics' for post-specific data) or provide clear exclusions. The guidance is present but not comprehensive.

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