LinkedIn Intelligence MCP Server

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions analyzing 'posting history' and providing 'data-driven recommendations,' but does not specify whether this is a read-only operation, what permissions are required, how long it takes, or if there are rate limits. For a tool with no annotation coverage, this leaves significant behavioral gaps.

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 purpose statement, elaboration on analysis scope, and a dedicated 'Args' section. It is appropriately sized at four sentences, with minimal redundancy. However, the 'Returns' statement could be integrated more seamlessly, and some sentences could be slightly tightened for efficiency.

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 (one parameter, output schema exists), the description provides a solid foundation. It explains the purpose, parameter semantics, and return value prioritization. With an output schema handling return details, the description does not need to elaborate on response structure. However, it could better address behavioral aspects given the lack of annotations.

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%, but the description includes an 'Args' section that documents the single parameter 'post_limit' with its default value and purpose ('Number of posts to analyze for recommendations'). This adds meaningful context beyond the schema, though it does not specify constraints like minimum/maximum values or format details.

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 personalized posting recommendations based on your content performance.' It specifies the verb ('Get'), resource ('posting recommendations'), and scope ('personalized...based on your content performance'). It distinguishes from siblings like 'analyze_optimal_posting_times' by focusing on personalized recommendations rather than general timing analysis.

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 mentioning 'personalized posting recommendations based on your content performance,' suggesting it should be used when seeking data-driven advice for content strategy. However, it lacks explicit guidance on when to use this versus alternatives like 'analyze_my_content_performance' or 'generate_my_content_calendar,' and does not specify prerequisites or exclusions.

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