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

Beyond the readOnlyHint annotation, it discloses that data comes from readings recorded about twice a day, so the agent knows the count may not be real-time. It also reveals the return structure (latest, start, change, per-day/week series) and the 90-day aggregation threshold – useful behavioral detail not present in 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?

Four sentences with no redundancy: purpose, usage guidance, return structure, and sibling distinction. Each sentence earns its place, and the most identifying information is front-loaded.

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?

For a single-parameter read-only tool with no output schema, the description covers all needed grounds: what data it returns, its resolution, common use cases, and its relationship to linkedin_analytics. There is no missing information that would block correct invocation.

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 already fully documents the single 'days' parameter with default and max. The description adds the behavioral consequence of choosing a window: series resolution drops to per-week for windows over 90 days. This is a meaningful addition beyond the schema's basic range.

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 opens with 'The user's own LinkedIn follower count over time' – a specific verb, resource, and scope. It explicitly names linkedin_analytics as lacking a follower metric, distinguishing it from the closest sibling tool. Examples of user questions ('how many followers do I have?') further cement the tool's purpose.

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 says 'Use it for every follower question' and provides three concrete question phrasings that should route here. It also gives an exclusion: 'linkedin_analytics has no follower metric, so never say follower growth is unavailable without calling this first,' which prevents incorrect deflection to another tool.

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