LinkedIn Custom MCP Server

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

Annotations only provide a title, so the description carries the full burden. It discloses the read-only nature through 'Fetch,' but lacks details on behavioral traits like rate limits, authentication needs, or error handling. No contradiction with annotations exists, but the description adds minimal value beyond the implied read operation.

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 a single, efficient sentence that front-loads the key action and resource, with no wasted words. It directly addresses the tool's function and parameter usage, making it easy to parse and understand quickly.

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, no nested objects) and the presence of an output schema (which handles return values), the description is reasonably complete. It covers the core purpose and parameter usage, but could improve by adding context on authentication or error cases, though annotations are minimal.

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?

With 0% schema description coverage and only 1 parameter, the description compensates by explaining the parameter's purpose ('company's profile information by its URN') and providing an example format ('urn:li:organization:123'). This adds meaningful semantics beyond the bare schema, though it could specify URN constraints or validation rules.

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 ('Fetch') and resource ('company's profile information'), and distinguishes it from sibling tools like 'linkedin_search_companies' by focusing on retrieval by URN rather than search. It provides a concrete example of the URN format, making the purpose 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 retrieval by URN, which helps differentiate it from search-based siblings (e.g., 'linkedin_search_companies'). However, it does not explicitly state when to use alternatives or any exclusions, such as whether it works for all company types or requires specific permissions.

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