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exchange-rates-mcp-server

Fx Get Rate

fx_get_rate
Read-onlyIdempotent

Get the exchange rate for a currency pair on a given date (default: latest). Returns the rate, the actual rate date (which may differ from the requested date on weekends/holidays — ECB publishes business days only), and source provenance. Cross-rates are triangulated through EUR automatically. A same-currency pair returns a rate of 1, dated to the same publication day any other pair would report for that date. Use fx_convert_currency when you want the converted amount; use this tool when you only need the rate number.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNoISO 8601 date (YYYY-MM-DD). Omit for the latest available rate. ECB data starts 1999-01-04. Future dates are not supported.
base_currencyYesISO 4217 base currency code (e.g. USD). Call fx_list_currencies to get valid codes.
quote_currencyYesISO 4217 quote currency code (e.g. EUR). The rate is expressed as "how many quote units per 1 base unit".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
rateNoExchange rate: units of quote currency per 1 unit of base currency.
errorNoPresent when the call failed. Absent on success.
sourceNoAlways "ECB via Frankfurter" — the upstream data provider.
rate_dateNoActual date of the rate returned.
rate_typeNoAlways "ECB reference (mid-market)" — these are reference rates, not tradeable bid/ask.
date_snappedNoTrue when the API returned a different date than requested — ECB silently snaps weekend/holiday requests to the prior business day.
base_currencyNoThe base currency code.
quote_currencyNoThe quote currency code.

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already supply readOnlyHint, openWorldHint, and idempotentHint, and the description goes beyond them by disclosing key behaviors: date shifting on non-business days, EUR triangulation for cross-rates, and same-currency pairs returning 1 with a consistent publication date. This is material context an agent needs to interpret results correctly and is not present in the schema or annotations.

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 concise (three sentences) yet packs essential information: core purpose, key behavioral caveats, and selective routing. The most important facts come first, and every sentence contributes to correct invocation or interpretation. No waste.

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

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (currency pairs, date handling, triangulation) and an existing output schema (which presumably documents return fields), the description covers all critical operational aspects: default behavior, date-shift caveat, same-currency edge case, and the presence of source provenance. It also names supporting tools for codes and conversion, making it complete for an agent to call correctly.

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

Parameters4/5

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

Schema coverage is 100% with clear descriptions for each parameter, so the baseline is 3. The tool description adds value by explaining the rate direction ('how many quote units per 1 base unit') and the default-date semantics for the date parameter, which reinforces and extends the schema without redundancy.

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 opens with a specific verb and resource ('Get the exchange rate for a currency pair on a given date') and immediately clarifies the core output (rate, actual rate date, source). It explicitly names the sibling fx_convert_currency for converted amounts, clearly distinguishing this tool from alternatives without needing to inspect schemas.

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

Usage Guidelines5/5

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

Explicit routing is given: 'Use fx_convert_currency when you want the converted amount; use this tool when you only need the rate number.' It also covers default behavior (latest date) and points to fx_list_currencies for valid codes, giving clear when-to-use guidance and a direct exclusion.

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

A4.6/5.0
Disambiguation5/5

Each tool has a distinct, non-overlapping purpose: conversion, single rate, bulk rates, timeseries, currency listing, and DataFrame inspection/query. The descriptions clearly differentiate between getting a rate vs converting an amount, and between inline vs DataCanvas-stored timeseries.

Naming Consistency5/5

All tools follow a consistent fx_verb_noun pattern (e.g., fx_get_rate, fx_convert_currency, fx_list_currencies). The two dataframe tools also adhere to fx_dataframe_verb, maintaining a uniform convention across the entire set.

Tool Count5/5

Seven tools is well-scoped for an exchange-rate server. Each tool addresses a needed operation (conversion, rates, timeseries, metadata) without redundancy or bloat, and adding DataFrame analysis feels justified for handling large datasets.

Completeness5/5

The surface covers the full lifecycle of exchange-rate queries: listing currencies, fetching single or bulk rates, converting with historical dates, and retrieving timeseries. The optional DataCanvas integration fills the gap for large-range analysis, leaving no obvious dead ends for common use cases.