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 states what the tool returns but lacks critical behavioral details: it doesn't specify whose profile is assessed (e.g., the current user's or another's), whether it requires authentication, if it has rate limits, or how suggestions are generated. The description is functional but misses key operational context.

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 extremely concise and well-structured: a single sentence stating the action, followed by a bulleted list of three specific return items. Every element earns its place with zero waste, making it easy to scan 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 has 0 parameters, 100% schema coverage, and an output schema exists (so return values needn't be detailed), the description is reasonably complete. However, it lacks behavioral context (e.g., authentication needs, target profile) that would be helpful for an agent, especially with no annotations. It's adequate but has clear gaps in operational guidance.

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 tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description appropriately focuses on outputs rather than inputs. A baseline of 4 is applied here since the description doesn't need to compensate for any parameter gaps, and it efficiently describes the return values.

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 ('calculate') and resource ('profile completeness score'), plus it lists the three key return values. It effectively distinguishes this tool from its many siblings, which focus on analysis, creation, deletion, or retrieval of specific profile data rather than calculating an overall completeness metric.

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 no guidance on when to use this tool versus alternatives. While it implicitly suggests usage for assessing profile completeness, it doesn't mention prerequisites (e.g., whether a profile must exist), exclusions, or how it relates to sibling tools like 'get_my_profile' or 'get_profile_sections' that might provide overlapping or complementary data.

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