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Glama

MCPFax Public-Data Utility API

Postal code lookup

v1_zip
Read-onlyIdempotent

Postal code lookup: ZIP/postal code → place(s) & coordinates. Source: Zippopotam.us. $0.005 per call · GET /v1/zip

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYesPostal code. Example: '90210'.
countryNoISO-3166 alpha-2 (default US). Example: 'US'.

Schema Changelog

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

  1. Changed2 schema fields changed
    • addedInput schema / properties / code / examples
      Added value: +[
      +  "90210"
      +]
    • addedInput schema / properties / country / examples
      Added value: +[
      +  "US"
      +]
  2. First observed

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so the safety profile is covered. The description adds useful context like the data source, per-call price, and HTTP verb, but does not disclose response format details, pagination behavior, or country handling beyond what the schema already notes.

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?

One compact, front-loaded sentence communicates the core operation, result type, source, cost, and endpoint with no filler. Every element earns its place.

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 lookup with no output schema, the description provides the essential return concept and enough constraints to call it correctly, especially with the schema covering defaults. The only notable gap is the absence of explicit differentiation from sibling geocoding tools.

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 both code and country having descriptions and examples, so the schema carries the parameter-documentation burden. The description does not add parameter-specific meaning beyond the generic postal-code-to-place transformation.

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

Purpose4/5

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

The description clearly states the verb-resource pair ('Postal code lookup') and the expected transformation ('ZIP/postal code → place(s) & coordinates'), which distinguishes it from address geocoding or reverse geocoding. It does not explicitly contrast itself with sibling tools, but the input type is sufficiently clear.

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

Usage Guidelines3/5

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

The description implies when to use it: when the user supplies a postal code and wants places/coordinates. However, it gives no explicit exclusions or alternative tool routing, such as when v1_geocode or v1_reverse_geocode might be more appropriate, so the guidance is inferred rather than stated.

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