Lists available CV sections with line counts so you can pick the right section for targeted queries.
List available CV section names with approximate line counts.
Helps AI assistants pick the right section for targeted queries.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
v1.0.9Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, establishing the read-only safety profile. The description adds that line counts are 'approximate,' which signals imprecision, and 'available' signals that the list reflects actual sections present. This adds some behavioral nuance beyond annotations, though it does not cover details like ordering or grouping, which the output schema may address.
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 description is two short sentences, front-loaded with the core function in the first sentence and the rationale in the second. No word is wasted, and both sentences contribute distinct, valuable information.
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?
Given the tool's simplicity (no parameters), the presence of an output schema, and annotations covering read-only/idempotent behavior, the description is nearly complete. It explains what the tool returns, why to use it, and the approximate nature of line counts; however, it does not clarify the relationship to the similarly named sibling 'get_cv_sections,' leaving a small contextual gap.
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 has zero parameters, so there are no parameter semantics to document. The schema description coverage is 100% vacuously, and the description does not need to compensate; the baseline for 0 params is 4.
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 uses the specific verb 'List' with a clear resource: 'available CV section names' and adds 'approximate line counts' to define the output. It clearly states the tool's function, but it does not explicitly differentiate itself from the similar sibling 'get_cv_sections,' so it stops short of 5.
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 second sentence, 'Helps AI assistants pick the right section for targeted queries,' gives a clear use case for the tool. It explains why an agent would call it, but it does not list any alternatives or when not to use it, which keeps it at a 4.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.