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 effectively describes key behavioral traits: it returns a list of matching job postings, mentions the use of an unofficial API, and includes a critical warning about bot detection with heavy use. However, it lacks details on error handling, pagination, or authentication requirements.

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 with a clear opening sentence, organized parameter explanations in bullet-like format, and a warning section. It is appropriately sized for an 8-parameter tool, though the parameter list is lengthy. Every sentence adds value, but some redundancy exists (e.g., 'Returns list of matching job postings' could be integrated more tightly).

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 complexity (8 parameters, no annotations, but has an output schema), the description is largely complete. It covers purpose, parameters, returns, and critical warnings. The output schema existence means return values need not be detailed in the description. However, it lacks information on error cases or rate limiting specifics beyond the bot detection warning.

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 schema description coverage is 0%, so the description must fully compensate. It provides detailed semantic explanations for all 8 parameters, including examples (e.g., 'Python Developer'), enumerated value mappings (e.g., job_type: 'F=Full-time'), defaults (e.g., listed_at: 86400), and constraints (e.g., limit max: 50). This adds significant value beyond the bare schema.

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 ('Search for job postings') and the resource ('on LinkedIn'), distinguishing it from sibling tools like 'search_people' or 'search_companies' which search different LinkedIn entities. The verb 'search' is precise and the platform context is explicit.

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 through the parameter explanations (e.g., filtering by job type, location), but does not explicitly state when to use this tool versus alternatives like 'get_job' (which appears to fetch a specific job) or other search tools. No explicit when-not-to-use guidance or prerequisites are provided.

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