v1_reverse_geocodeReverse geocoding: Coordinates → nearest address. Source: Nominatim / BigDataCloud. $0.005 per call · GET /v1/reverse-geocode
| Name | Required | Description | Default |
|---|---|---|---|
| lat | Yes | Latitude. Example: '34.05'. | |
| lon | Yes | Longitude. Example: '-118.24'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover the read-only, idempotent nature of the tool. The description adds valuable behavioral context beyond annotations: the data sources (Nominatim/BigDataCloud), the cost per call ($0.005), and the HTTP method (GET). It does not mention response format or errors, but the added operational detail earns a solid score.
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 efficient sentence that front-loads the core operation, then appends three useful metadata facts: source, cost, and HTTP endpoint. Every clause contributes value with zero redundancy.
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 simple two-parameter, read-only, idempotent tool, the description combined with annotations and schema is nearly complete. It covers the core behavior, cost, source, and endpoint. The only notable gap is the response format, but since there is no output schema and the tool is simple, this is a minor omission.
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 input schema covers 100% of parameters with descriptions and examples for lat and lon. The description ties the parameters to the coordinate-to-address operation, but it does not add new meaning beyond the schema. With full schema coverage, the baseline of 3 is appropriate.
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 operation as reverse geocoding, expressed as 'Coordinates → nearest address'. This is specific and makes the tool's purpose immediately obvious. It implicitly differentiates from sibling v1_geocode, though it does not name it explicitly.
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 clearly implies the use case: converting latitude/longitude to a nearby address. It does not provide explicit when-to-use/when-not-to-use guidance or name alternatives like v1_geocode, but the semantic clarity gives an agent enough context to choose it correctly.
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.
Every tool targets a distinct resource or operation—geocoding, weather, DNS, VIN, stock quotes, etc.—with no meaningful overlap in purpose. Even the location- and finance-related tools are clearly separated by their descriptions.
All tools follow the same v1_<resource>[_modifier] snake_case pattern, such as v1_air_quality, v1_reverse_geocode, and v1_validate_email. Although the names are not verb-based, the convention is perfectly consistent across all 31 tools.
31 tools exceeds the 25+ threshold and creates a heavy selection burden for agents, even though the server's stated purpose is broad. Many endpoints are small single-purpose lookups that could be grouped into fewer combined tools without losing clarity.
As a general public-data utility, the set covers a wide range of common lookup categories: location, weather, finance, legal, health, business, internet, and reference data. It has minor gaps like historical financial time series or phone-number validation, but no obvious dead ends since all tools are self-contained read-only lookups.