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 mentions that it 'Returns list of company posts/updates,' which gives some output context, but it doesn't cover important aspects like rate limits, authentication needs, pagination, error handling, or data freshness. For a read operation with zero annotation coverage, this leaves significant gaps in understanding the tool's behavior.

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 concise, with a clear purpose statement followed by parameter details and return information. Every sentence adds value without redundancy, and it's front-loaded with the main functionality. The bullet-point style for args enhances readability without unnecessary verbosity.

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 (2 parameters, no nested objects) and the presence of an output schema (which handles return value documentation), the description is reasonably complete. It covers the purpose, parameters, and output at a high level. However, the lack of behavioral details (e.g., rate limits, error cases) and usage guidelines relative to siblings prevents a perfect score.

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 context for both parameters beyond the input schema, which has 0% description coverage. It explains that 'public_id' is the 'Company's public identifier (URL slug)' and 'limit' has a 'default: 10, max: 50.' This clarifies usage and constraints effectively, compensating well for the lack of schema descriptions.

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 recent posts/updates from a company page.' It specifies the verb ('Get') and resource ('posts/updates from a company page'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'get_company' or 'get_profile_posts', which would be needed for a perfect score.

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_company' (which might retrieve company metadata) or 'get_profile_posts' (which might retrieve posts from individual profiles), nor does it specify prerequisites or contexts for usage. This lack of comparative guidance limits its effectiveness.

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