LinkedIn Intelligence MCP Server

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden and does well by detailing what information is returned (token expiry, cookie freshness, features, recommended actions). It clearly indicates this is a read-only diagnostic tool without side effects, though it doesn't mention rate limits, permissions needed, or error behaviors.

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 efficiently structured with a clear opening sentence stating the purpose, followed by a bulleted list of return details. Every sentence earns its place by adding specific value about what the tool provides, with no redundant or vague language.

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 simple nature (0 parameters, output schema exists), the description is complete enough. It explains the purpose, what status information is returned, and includes actionable output (recommended actions). With an output schema handling return structure, no additional detail about return values is needed.

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 no parameter documentation is needed. The description appropriately adds no parameter information, focusing instead on output semantics. A baseline of 4 is applied since zero parameters require no compensation.

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 with a specific verb ('Get') and resource ('LinkedIn authentication status'), distinguishing it from all sibling tools which perform content analysis, posting, profile management, or data retrieval operations. It explicitly covers both official and unofficial APIs, making its scope unambiguous.

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 by specifying it returns authentication status for both API types, suggesting it should be used to check authentication state before performing other LinkedIn operations. However, it does not explicitly state when not to use it or name alternative tools for similar purposes, though no obvious alternatives exist among siblings.

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