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

With no annotations, the description carries the full behavioral burden. It discloses cost ('Costs 5 credits per call'), but this is misleadingly absolute because the schema states cached results are returned free unless refresh=true. It also omits read-only safety, caching behavior, and return characteristics, leaving important operational context to the schema.

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 short sentences with no waste. The core action is front-loaded, followed by a concise use case and cost note.

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 simple two-parameter read tool, the description covers the action and a use case. However, without annotations or an output schema, it should say more about what a result contains, how far back 'recent' extends, and how caching affects returned data and cost.

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%, so both linkedin_url and refresh are already documented in the schema. The description adds no parameter-level meaning beyond what is structurally provided, which fits the baseline of 3 when the schema does the heavy lifting.

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 states a specific verb and resource ('Fetch the recent posts a LinkedIn person has reacted to') and immediately clarifies the value ('inferring interests'). It distinguishes reactions from authored posts or comments well enough for an agent to select it over siblings like linkedin_profile_posts or linkedin_profile_comments.

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 phrase 'Useful for inferring interests' gives implied usage context, but there is no explicit when-to-use guidance, no exclusions, and no named alternatives such as linkedin_profile_posts or linkedin_post_reactions. The agent must infer the appropriate scenario.

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