IP geolocation
v1_ip_geolocationIP geolocation: IP address → location, ASN, ISP. Source: ipwho.is / ip-api. $0.005 per call · GET /v1/ip-geolocation
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| ip | Yes | IPv4 or IPv6 address. Example: '8.8.8.8'. |
v1_ip_geolocationIP geolocation: IP address → location, ASN, ISP. Source: ipwho.is / ip-api. $0.005 per call · GET /v1/ip-geolocation
| Name | Required | Description | Default |
|---|---|---|---|
| ip | Yes | IPv4 or IPv6 address. Example: '8.8.8.8'. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / ip / examplesAdded value: +[
+ "8.8.8.8"
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already signal read-only, idempotent, and non-destructive behavior. The description adds value beyond annotations by naming the external data sources (ipwho.is / ip-api) and the cost per call, giving agents awareness of the external dependency and pricing. It does not cover failure modes or caching, but this is acceptable for a simple read-only lookup.
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 line that front-loads the core function, then adds source, cost, and endpoint details without redundancy. Every element earns its place and there is no filler.
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 one-parameter tool with no output schema, the description provides the essential return fields (location, ASN, ISP), identifies the upstream source, and notes the cost. It omits a detailed response format and error behavior, but for this simple read-only tool, the information is largely sufficient.
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 coverage is 100%, and the 'ip' parameter is already documented as an 'IPv4 or IPv6 address' with an example. The description does not add parameter-level detail beyond the arrow notation, so the schema carries the semantic load and the baseline score applies.
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 the exact transformation ('IP address → location, ASN, ISP'), making the tool's function concrete. The IP-specific input plus ASN/ISP output clearly distinguish it from address-based geocoding siblings like v1_geocode and v1_reverse_geocode.
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 use case is implied: when an agent needs location, ASN, or ISP data from an IP address, this tool is the match. However, there are no explicit when-to-use or when-not-to-use statements, and no direct comparison with v1_geocode or v1_reverse_geocode.
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.