list_skill_gaps

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

Annotations already establish readOnly/idempotent/non-destructive, so the bar is lower, and the description adds real value: it discloses the return shape (counts, on_resume flags, missing must-haves, fit-score gaps no course closes) and an agent-facing behavioral rule about not recommending unsupported skills. It does not cover pagination or result-size limits, which keeps it short of a 5.

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?

The opening is well front-loaded with what the tool returns, but the middle is a sprawling run-on with nested parentheticals enumerating output fields and every learning platform (Coursera, LinkedIn Learning, edX, nearby colleges). The detail is defensible given there is no output schema, but the structure is dense enough that an agent must parse several clauses to find the routing guidance.

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 correctly carries the burden of describing the return payload and does so thoroughly, and read-only annotations cover the safety profile. What remains thin is the timeline parameter's behavior when omitted (it defers to the watchlist's learning.timeline without explaining the effect).

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?

There is a single optional parameter whose enum and default are fully documented in the schema (100% coverage), so the schema does the heavy lifting. The description only indirectly references it ('whether it fits the timeline') without adding format or defaulting behavior beyond what the schema already states; 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?

The description gives a precise verb+resource (list skill gaps across the person's matching jobs and applications) and immediately specifies the ordering ('most in demand first') and the per-skill payload (on_resume, job counts, missing must-haves, learning links). It explicitly separates itself from get_job and get_interview_prep, so an agent can route correctly without opening any 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 states the scope (all matching jobs and applications, aggregated) and names two concrete alternatives with the condition that selects each: 'For one job's gaps use get_job; to prepare for a call use get_interview_prep.' It also states a hard usage constraint ('Recommend only from these links; never suggest claiming a skill the resume doesn't show').

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