Identifies missing skills and job requirements a student lacks, ranked by importance and how often they cost a job, and excludes skills already in their profile.
What to learn next: requirements the student lacks, weighted by importance and how often they cost a job. Skills already in the profile are excluded (those are CV-wording problems, not learning gaps).
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It usefully discloses the ranking logic (importance x how often the gap cost a job) and an exclusion rule (profile skills are filtered out), which is real behavioral insight. It does not state that the operation is read-only, nor how gaps are derived or how many are returned.
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, front-loaded with the outcome ("What to learn next") before the mechanics. The exclusion clause earns its place by preventing a common misinterpretation, though the phrasing "those are CV-wording problems" is slightly informal.
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, annotations, or parameters, so the description is the only specification an agent has. It conveys the concept and ranking but never describes the shape of the return (fields, count, ordering guarantees, empty case), leaving a real gap for a tool whose whole value is its output.
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 tool takes zero parameters, so there is nothing for the description to disambiguate; the baseline for a no-parameter tool applies. The description makes clear the call needs no input.
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?
"What to learn next: requirements the student lacks" names the resource and what the result set consists of, and the weighting clause specifies how those items are ranked. It also implicitly separates itself from CV-oriented siblings by excluding skills already in the profile, though the tool's own action (compute/derive vs. fetch) is never stated with a verb.
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?
"What to learn next" implies the use case (planning skill development), and the parenthetical points away from CV-wording problems. But no sibling is named as an alternative and no explicit when-to-use/when-not-to-use condition is given relative to tools like cv_writing_guide or job_score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.