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FX Historical Rate

fx_historical
Read-onlyIdempotent

Reference rate for a specific past date. Useful for 'what was USD→EUR on 2024-11-15?' questions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
baseNoEUR
rate_dateYesISO date, e.g. 2024-11-15.

Schema Changelog

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

  1. First observed

TDQS

A3.5/5.0
Behavior3/5

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 covered. The description adds that the tool returns a 'reference rate' for a past date, which is behaviorally consistent. It does not disclose potential edge cases like weekend/holiday rates, source, or rate timing, but given the strong annotation coverage, the added context meets the minimum bar.

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 exactly two sentences with no redundant words. The purpose statement is front-loaded, and the example sentence adds practical value. Every part earns its place.

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

Completeness3/5

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

For a simple, read-only, 2-parameter tool, the description plus annotations cover the safety profile and give a concrete example. However, the base parameter remains unexplained, and with no output schema, the return value's shape is unknown. These are notable gaps for an agent trying to invoke the tool correctly.

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

Parameters2/5

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

Schema description coverage is only 50%: rate_date has a format example in the schema, but base has no schema description. The tool description's example 'USD→EUR' hints at currency pairing but does not explain the base parameter's meaning or how the quote currency is specified (there is no quote parameter). This leaves the base parameter ambiguous and fails to compensate for the schema gap.

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 states the tool returns a 'reference rate for a specific past date' and gives a concrete example ('what was USD→EUR on 2024-11-15?'). This clearly conveys the action and resource. It implicitly distinguishes from siblings like fx_timeseries via the phrase 'specific past date', but it does not name the alternatives explicitly, so it falls just short of full sibling differentiation.

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 an explicit use case: answering single-date historical FX questions like 'what was USD→EUR on 2024-11-15?'. This gives clear context for when to use it. However, it does not state when not to use it or point to alternatives (e.g., fx_timeseries for ranges, fx_convert for current or forward conversions), so exclusions are missing.

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

B3.2/5.0
Disambiguation2/5

Multiple tools have genuinely blurry boundaries: company_change vs company_changes differ only by singular/plural yet serve different purposes, company_domain vs company_classify vs company_lookup_auto all accept a domain, geo_zip_lookup vs geo_enrich vs geo_zip_batch all return ZIP profiles, and email_validate subsumes much of email_disposable and email_free_provider. The domain prefixes help narrow search space, but within many domains an agent cannot reliably predict which tool is the right one.

Naming Consistency4/5

All 129 tools uniformly follow a snake_case [domain]_[topic] convention (company_, fx_, geo_, dns_, weather_, tax_), which is highly predictable and consistent. Minor deviations include the confusing company_change/company_changes pair, and inconsistent suffix usage (_batch appears on address_validate_batch, company_domains_batch, geo_zip_batch but not on equivalent lookup tools elsewhere).

Tool Count1/5

129 tools far exceeds the 50+ extreem-mismatch threshold, bundling roughly 28 unrelated data domains (weather, fx, tax, ccompany, dns, jobs, flight, email, phone, tax...) into a single MCP surface. Even focusing on one domain forces the agent to load an enormous unrelated tool list; this should be split into many smaller domain-specific servers.

Completeness4/5

Per-domain coverage is impressively thorough: weather spans current/forecast/hourly/historical/normals/marine/route/air-quality, fx covers rates/convert/historical/volatility/correlation/strenth, and company includes lookup/enrichment/networks/timeline/peer-comparison plus six buyer-tuned signals with profile-introspection tools. Minor gaps like flight being historical-only and smtp probes skipping major email providers are documented scope decisions rather than dead ends.

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