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 describes the data source (LinkedIn via Professional Network Data API) and return format ('organized by category'), which adds useful context. However, it doesn't disclose behavioral traits like rate limits, authentication needs, data freshness, or error conditions, which are important for a read operation with external API dependencies.

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 core purpose, followed by context about the API and insights, then clearly labeled '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 low complexity (1 parameter) and the presence of an output schema (which handles return values), the description is reasonably complete. It covers purpose, parameter semantics, and data context. However, for a tool with no annotations, it could better address behavioral aspects like permissions or limitations to fully compensate for the lack of structured metadata.

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 description adds meaningful semantics beyond the input schema: it explains that 'profile_id' is a 'LinkedIn public ID' with an example ('e.g., "johndoe"'), which clarifies the parameter's purpose and format. Since schema description coverage is 0% (no schema descriptions), this compensates well for the single parameter, though it doesn't detail validation rules or constraints.

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 interests including influencers, companies, groups, and topics.' It specifies the verb ('Get') and resource ('profile interests') with concrete categories. However, it doesn't explicitly differentiate from sibling tools like 'get_profile' or 'get_profile_sections' beyond mentioning this data is unique to the Professional Network Data API.

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 by stating this data provides 'insights into what/who a person follows on LinkedIn,' suggesting it's for analyzing user interests. It doesn't provide explicit when-to-use guidance versus alternatives (e.g., when to use this over 'get_profile'), nor does it mention prerequisites or exclusions, leaving usage context somewhat implied rather than clearly defined.

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