suggest_area_leavesFind leaf regions such as cities or districts within an area by name prefix, so you can choose a concrete location for searches and filters.
Suggest leaf regions (cities/districts without children) inside area_id by name prefix; used to pick concrete locations.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | ||
| area_id | No |
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 must carry the behavioral burden. It discloses that results are leaf regions matched by name prefix, but does not state that the operation is read-only, how many suggestions are returned, or what happens when area_id is null.
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 a single compact sentence with the primary purpose front-loaded and no wasted wording. The semicolon cleanly adds the intended use case without cluttering the core definition.
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 low-complexity suggestion tool with two parameters and no output schema, the description covers the result type and matching basis. However, it omits what happens when area_id is omitted, which is material because the schema allows null and defaults to null.
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 0%, so the description must compensate. It explains that text is matched as a name prefix and area_id scopes the search, but it does not explain the optional/default-null behavior of area_id, leaving a gap for one of two parameters.
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 specific verb-resource pair: suggesting leaf regions, clarified as cities/districts without children, inside area_id by name prefix. It semantically distinguishes this from broader sibling tools like suggest_areas, though it does not explicitly name the alternative.
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 'used to pick concrete locations' implies a use case, but there is no explicit guidance on when to choose this over suggest_areas or dict_districts. No exclusions or prerequisites are stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.