check_linkedin_aboutCheck a LinkedIn About section: what shows before "see more" on a phone and on a computer, whether the opening says who they are, results with numbers, buzzwords, a clear next step, and the 2,600-character limit.
| Name | Required | Description | Default |
|---|---|---|---|
| role | No | Optional. The job title the user wants to be found for. | |
| about | Yes | The whole About section. |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), and the description adds real behavioral substance by disclosing the evaluation checklist the agent's input will be judged against. It does not describe output shape or scoring thresholds, but it goes beyond restating annotations.
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?
A single front-loaded sentence with a colon-led list; every item (preview truncation, identity, numbers, buzzwords, CTA, character limit) carries distinct information. Slightly dense but no filler.
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?
With no output schema, the description compensates by naming the dimensions the check reports on, which is close to describing the return content. An agent knows what to send and roughly what it gets back; only scoring format and edge cases remain unspecified.
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?
Schema description coverage is 100% for both parameters, so the schema already documents 'about' (whole section) and the optional 'role' (job title to be found for). The description adds no syntax, format, or interaction detail beyond what the schema provides, so the baseline 3 applies.
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?
States a specific verb (check) and resource (LinkedIn About section) and enumerates the exact facets evaluated: mobile/desktop 'see more' truncation, opening identity statement, quantified results, buzzwords, CTA, and the 2,600-character limit. This clearly separates it from check_linkedin_headline and the other check_* siblings.
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?
Usage is implied (you have an About section and want it reviewed), but there is no explicit when-to-use statement, no prerequisites, and no routing to alternatives such as check_linkedin_headline or generate_linkedin_headline for a headline problem. Adequate but leaves selection to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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