LinkedIn 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 the default behavior for the logged-in user, which is useful context, but does not cover other traits like authentication needs, rate limits, error handling, or data freshness. This leaves gaps in understanding the tool's operational 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 front-loaded with the core purpose and includes only essential details (content enumeration and default behavior) in two efficient sentences. There is no redundant or verbose language, making it highly concise and well-structured.

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 moderate complexity (fetching profile data with one optional parameter), no annotations, and no output schema, the description is incomplete. It lacks details on return format (e.g., structure of experience/education data), error scenarios, or prerequisites like login status, which are necessary for full contextual understanding.

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 100% description coverage, so the schema already documents the single parameter 'profile_url' with its purpose and optional nature. The description adds no additional parameter semantics beyond what the schema provides, such as format examples or edge cases, resulting in the baseline score of 3.

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 specific action ('Get') and resource ('LinkedIn user profile summary') with detailed content enumeration ('name, headline, location, about section, experience, and education'). It distinguishes from siblings like 'get_connections' or 'get_job_details' by focusing on comprehensive profile data rather than connections, job details, or other LinkedIn entities.

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 clear context for when to use this tool: to fetch profile summaries, with a default behavior for the logged-in user. However, it does not explicitly state when not to use it or name alternatives (e.g., using 'get_connections' for network data instead of profile details), which prevents a perfect score.

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