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

Annotations already declare readOnlyHint, openWorldHint, and destructiveHint=false, so safety is covered. The description adds genuine behavioral context: the query runs against 'live' market data and returns a three-part analytical result (share of vacancies, skills held, missing skills ranked by vacancy unlock), which is the only place the return shape is disclosed since no output schema exists.

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?

Front-loads the verb and resource, then packs the three result metrics into one structured clause; the closing disambiguation sentence is short and load-bearing. Slightly dense but nothing is redundant.

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 tool with a large nested filter object and no output schema, the description supplies the essential missing pieces: the nature of the live data source, the result metrics, and the measured-vs-filter distinction. It omits only secondary details such as filter combination semantics and behavior on empty input.

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 coverage is 100%, so the baseline is 3, but the description earns a bump by resolving a real ambiguity: `skills` is the MEASURED set, not a filter, distinguishing it from `filters.skills`. It still does not explain how the ~30 facet filters combine (AND vs OR) or what happens with an empty skill list.

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 (score a skill list) against a specific resource (live open-vacancy market) scoped by a filtered role, and enumerates the three outputs: coverage share, must-have skills held, and missing skills that unlock vacancies. This is clearly distinguishable from siblings like facets or job_market_stats.

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 rather than stated: the agent must infer that filters come from the facets tool (only noted in the schema) and that this is a measurement query, not a search. There is no explicit when-to-use/when-not guidance or named alternative sibling.

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