v1_geocodeForward geocoding: Place name → coordinates, country, timezone. Source: Open-Meteo / Nominatim. $0.005 per call · GET /v1/geocode
| Name | Required | Description | Default |
|---|---|---|---|
| q | Yes | Place name to search. Example: 'London'. | |
| count | No | Max results 1-10. Example: '3'. | |
| country | No | ISO-3166 alpha-2 filter. Example: 'GB'. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / count / examplesAdded value: +[
+ 3
+]Input schema / properties / country / examplesAdded value: +[
+ "GB"
+]Input schema / properties / q / examplesAdded value: +[
+ "London"
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds valuable context beyond annotations: the data source (Open-Meteo / Nominatim), the cost per call ($0.005), and the HTTP method/endpoint (GET /v1/geocode). It does not disclose rate limits or attribution requirements, but these are not critical for basic selection.
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 concise sentence that front-loads the core purpose, then adds source, pricing, and HTTP method without waste. Every piece of information earns its place.
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, read-only geocoding tool with a fully documented schema, the description covers the essential behavior: input type, output fields, source, and cost. It does not detail the exact response envelope or error behavior, but those are not necessary for correct selection and invocation here.
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?
Input schema coverage is 100%, so the schema already documents all three parameters with examples and descriptions. The description adds general context about what is returned but does not need to restate parameter details.
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 states a specific operation: forward geocoding converting a place name into coordinates, country, and timezone. It clearly distinguishes this from the sibling v1_reverse_geocode and the broader geolocation-related tools.
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 'Forward geocoding' clearly signals the input-output direction (place name to coordinates), which implies when this tool should be used versus reverse geocoding. It does not explicitly name alternatives or exclusions, but the context is clear enough for an agent to select 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.
Each tool maps to a distinct public data source and lookup type, from weather and geocoding to legal codes and vehicle VINs. Even adjacent tools like weather vs. weather_alerts or geocode vs. reverse_geocode are clearly separated by resource and direction.
All tools share a consistent v1_ prefix and snake_case resource naming, making the pattern predictable. Minor inconsistencies exist: most names are noun phrases (v1_country, v1_stock_quote) while a few are verb-led (v1_geocode, v1_validate_email, v1_unit_convert), and some abbreviations like v1_cfr and v1_lei are less descriptive.
At 31 tools, the surface is large and will strain agent tool-selection, even though each tool is individually useful. The broad 'public data utility' scope explains the count, but the calibration threshold of 25+ tools indicates an oversized set for practical use.
For a lookup-oriented public data utility, the set covers a wide range of common needs—weather, finance, location, legal/medical codes, domain/network, and conversions—without dead ends. It lacks some obvious public data categories (e.g., web search, population/census, news) and enumeration endpoints, but agents can work around these gaps.