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

Annotations already declare readOnly/idempotent, so the safety profile is covered. The description adds real semantic context: the score is 'latest' (freshness), is LinkedIn's own metric rather than a platform computation, and is not a campaign-performance measure — these prevent misreading the returned numbers.

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?

Two sentences, zero padding: the primary payload is front-loaded, and the disambiguation clause follows immediately. Every clause earns its place.

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?

There is no output schema, so the description carries the burden of describing returns — it does state the score and its four components, which is sufficient shape information. It does not mention behavior when no SSI has ever been computed for the account, a small remaining gap.

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?

Only one parameter and it is fully documented in the schema (source tool for obtaining `linkedAccountId`). The description adds no syntax, format, or validity information beyond that, so 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 resource and scope: the account's latest LinkedIn SSI score plus its four component scores. It also explicitly disambiguates from analytics tools, so an agent can distinguish it from `linkedin_account_stats` and `analytics_campaign` without opening a schema.

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?

It clearly says what this tool is NOT for and names the two alternatives to use instead for campaign performance. What is missing is a positive 'use this when' trigger, but that is largely implied by the named resource (SSI).

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