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 full burden. It states the tool analyzes audience demographics but doesn't disclose behavioral traits like whether it's read-only (implied but not explicit), what data sources it uses (commenters' profiles only), rate limits, permissions required, or format of returned demographics. For a tool with no annotation coverage, this leaves significant gaps.

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 appropriately sized and front-loaded: the first sentence states the purpose clearly, followed by Args and Returns sections. Every sentence adds value, though the structure could be more integrated (e.g., combining into a single paragraph). No wasted words.

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 1 parameter with 0% schema coverage and an output schema exists (so return values are documented elsewhere), the description is minimally adequate. It covers the purpose and parameter semantics but lacks behavioral context (no annotations) and usage guidelines. For a read-like analysis tool, it's passable but incomplete.

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 schema only indicates a required string parameter 'post_urn'. The description adds meaning by specifying it's a 'LinkedIn post URN', which clarifies the parameter's purpose and format beyond the bare schema. However, it doesn't explain URN structure or provide examples, leaving some ambiguity.

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: 'Analyze the audience engaging with a specific post' and specifies it returns 'audience demographics based on commenters' profiles.' This is a specific verb+resource combination that distinguishes it from siblings like analyze_content_performance or analyze_engagement. However, it doesn't explicitly differentiate from analyze_my_content_performance which might also involve audience analysis.

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 post URN), exclusions, or compare it to similar tools like analyze_engagement or get_post_analytics. The agent must infer usage from the purpose alone.

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