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Glama

MCPFax Public-Data Utility API

Currency exchange rates

v1_fx
Read-onlyIdempotent

Currency exchange rates: Reference FX rates (ECB) with optional amount conversion. Source: Frankfurter (ECB) / open.er-api. $0.005 per call · GET /v1/fx

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toYesTarget currency/currencies (comma-separated). Example: 'EUR,GBP,JPY'.
fromYesBase currency ISO-4217. Example: 'USD'.
amountNoAmount to convert (default 1). Example: '100'.

Schema Changelog

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

  1. Changed3 schema fields changed
    • addedInput schema / properties / amount / examples
      Added value: +[
      +  100
      +]
    • addedInput schema / properties / from / examples
      Added value: +[
      +  "USD"
      +]
    • addedInput schema / properties / to / examples
      Added value: +[
      +  "EUR,GBP,JPY"
      +]
  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 readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is known. The description adds valuable behavioral context beyond annotations: it identifies the rates as 'Reference FX rates (ECB)' (implying they are reference, not necessarily real-time market rates), names the sources (Frankfurter/ECB and open.er-api), and notes the per-call cost. This helps an agent understand the nature of the data and cost implications.

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 a single, tight sentence that front-loads the main function, then provides source, cost, and endpoint details. Every phrase earns its place, with no filler or redundancy. It is compact and immediately scannable for an agent.

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 read-only tool with 3 parameters fully documented in the schema, the description is nearly complete. It includes source, pricing, and endpoint, which are useful operational details. It does not describe the response format or whether historical rates are supported, but given the absence of an output schema and the low complexity, these are minor gaps.

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 clear descriptions and examples for from, to, and amount. The description adds only the notion of 'optional amount conversion,' which is already encoded in the schema's 'Amount to convert (default 1)'. With the schema doing the heavy lifting, a baseline score of 3 is appropriate; the description does not introduce new parameter-level meaning.

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 clearly states the tool provides 'Reference FX rates (ECB) with optional amount conversion,' specifying the resource (currency exchange rates) and the key feature (amount conversion). It is distinct from sibling tools like v1_unit_convert and v1_crypto_price based on the explicit 'FX rates' scope. The title and description together leave no 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 provides clear context: this is for currency exchange rates and conversions, and it mentions the data source and pricing. It does not explicitly mention when not to use it or name alternatives, but the purpose is sufficiently specific for an agent to select it for currency-related requests. No misleading usage guidance is present.

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