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 of behavioral disclosure. It states the tool returns a list of users and reaction types, which is useful, but doesn't cover critical aspects like whether this is a read-only operation, rate limits, authentication needs, or pagination behavior. For a tool with zero annotation coverage, this is insufficient.

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 appropriately sized. It starts with a clear purpose statement, followed by an 'Args' section with parameter details and a 'Returns' statement. Each sentence adds value, with no wasted words, though minor improvements in flow could elevate it to a 5.

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 adequate but has gaps. It explains the parameter well but lacks behavioral context (e.g., safety, limits) and usage guidelines. With no annotations, it doesn't fully compensate for missing structured data.

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 significant value beyond the input schema, which has 0% description coverage. It explains that 'post_urn' is a 'LinkedIn post URN' and provides an example format ('e.g., "urn:li:activity:123456789"'), clarifying the parameter's purpose and syntax. With only one parameter, this compensation is effective, though not exhaustive.

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 reactions/likes on a specific post.' It specifies the verb ('Get'), resource ('reactions/likes'), and scope ('on a specific post'), making it easy to understand. However, it doesn't explicitly differentiate from sibling tools like 'get_post_comments' or 'get_post_analytics', which prevents a score of 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 provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_post_comments' for comments or 'get_post_analytics' for broader analytics, nor does it specify prerequisites or exclusions. This leaves the agent without context for tool selection.

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