geocodeTurn a place name, postal code, or "code, CC" string into { lat, lon, label }. Auto-detects US/CA/GB/NL/JP/BR postal patterns; ambiguous digit-only codes default to US unless the caller appends a country code (e.g. "10115, DE"). Falls back to Open-Meteo place-name search for everything else.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | City name, postal code, or "CODE, CC" (e.g. "75001, FR"). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and excels: it discloses the auto-detection logic, the US default for ambiguous codes, the need for a country code, and the fallback to Open-Meteo search. This goes beyond a simple 'resolve a place' statement and sets clear expectations for edge cases.
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 sentences, with the core purpose front-loaded and no filler. Every sentence contributes to understanding the tool's behavior and usage.
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 a simple one-parameter tool with no output schema, the description is notably complete: it specifies the output shape ({ lat, lon, label }), covers input patterns, explains ambiguity handling, and describes the fallback behavior. There are no significant gaps for an agent to invoke the tool correctly.
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 schema already covers 100% of the parameter with a description, meeting the baseline. The tool description adds meaningful nuance by explaining the 'code, CC' format and the defaulting behavior for digit-only codes, which helps the agent format queries correctly beyond the schema's static example.
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 clearly states the tool's verb 'Turn' and resource 'a place name, postal code, or code, CC string' into an object with lat, lon, and label. It is specific and immediately differentiates from sibling tools like calculate and describe_skin_types.
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 description provides clear context on how to use the tool, including the auto-detection of postal patterns and the directive to append a country code for ambiguous digit-only codes. It does not explicitly name alternatives or when-not-to-use scenarios, but the guidance is practical and sufficient for a single-purpose geocoding tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a distinct role: calculate is the core computation, describe_skin_types provides input for skin type, and geocode converts location to coordinates. There is no overlap or ambiguity between them.
Tool names are imperative verbs in lowercase, with snake_case for multi-word names (describe_skin_types). While 'calculate' and 'geocode' are single verbs without a noun, the pattern is consistent enough and easy to predict.
Three tools is a well-scoped size for a focused vitamin D calculator server. Each tool is necessary and there are no redundant additions.
The server covers the full user journey: geocoding location, determining skin type, and calculating vitamin D synthesis with detailed outputs. No obvious dead ends or missing operations within the stated purpose.