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Glama

MCPFax Public-Data Utility API

Reverse geocoding

v1_reverse_geocode
Read-onlyIdempotent

Reverse geocoding: Coordinates → nearest address. Source: Nominatim / BigDataCloud. $0.005 per call · GET /v1/reverse-geocode

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latYesLatitude. Example: '34.05'.
lonYesLongitude. Example: '-118.24'.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed2 schema fields changed
    • addedInput schema / properties / lat / examples
      Added value: +[
      +  34.05
      +]
    • addedInput schema / properties / lon / examples
      Added value: +[
      +  -118.24
      +]
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior. The description adds valuable context beyond those: the upstream providers (Nominatim / BigDataCloud), the per-call cost, and the HTTP GET endpoint. It does not contradict any annotation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely compact and front-loaded: purpose first, then source, cost, and endpoint. Every clause contributes information without filler, making it easy for an agent to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

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 geocoding tool with rich annotations and full schema coverage, the description is nearly complete. It lacks explicit output-format details, but no output schema exists and the phrase 'nearest address' conveys the expected result. Minor gaps like error handling or no-result behavior prevent a 5.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with examples and descriptions for both lat and lon. The tool description itself does not add extra parameter-level meaning, but it does not need to because the schema already documents the parameters fully. Baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific transformation ('Coordinates → nearest address') and reinforces it with the 'Reverse geocoding' label. This clearly differentiates from sibling v1_geocode and other tools without ambiguity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly communicates the direction of the tool (coordinates in, address out), which tells an agent when to use it versus the likely forward-geocoding sibling. It does not explicitly name alternatives or exclusions, but the context is unambiguous for the main use case.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.7/5.0
Disambiguation5/5

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.

Naming Consistency4/5

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.

Tool Count2/5

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.

Completeness4/5

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.

Resources