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Glama

Bnr Exchange Rates

bnr_exchange_rates
Read-onlyIdempotent

Latest National Bank of Romania (BNR) official FX reference rates, parsed from BNR's daily XML feed. Returns the reference date plus an array of currency rates. Each rate is RON (Romanian leu) per multiplier units of the currency (multiplier is 1 unless noted, e.g. 100 for HUF/JPY/KRW), so the per-1-unit rate = value / multiplier. Currencies are ISO 4217 codes (plus XAU gold, XDR SDR).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds behavioral context beyond annotations, such as the multiplier logic, ISO 4217 codes (plus XAU/XDR), and that rates are RON per multiplier units.

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?

Three sentences pack all essential information: source, content, format, multiplier, and currency codes. No fluff or redundancy.

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 no input parameters and no output schema, the description sufficiently explains the output format and nuances (multiplier, ISO codes). Completeness is high for a simple data retrieval tool.

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?

No parameters exist (0 params, schema coverage 100%), so baseline is 4. The description adds meaning about the output structure: reference date, array of currency rates, multiplier details, and currency codes.

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 returns the latest BNR official FX reference rates, parsing from BNR's daily XML feed. It specifies the source, content, and format, distinguishing it from siblings like bnr_exchange_rates_history.

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 implicitly indicates use for current rates but does not explicitly mention when to use this vs alternatives (e.g., bnr_exchange_rates_history for historical rates). No when-not guidance is provided.

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

A4.1/5.0
Disambiguation4/5

Most tools have distinct purposes, but some overlap exists between ask_pipeworx, ask_pipeworx_grounded, deep_research, and validate_claim, which could cause confusion. However, the descriptions help clarify when to use each.

Naming Consistency3/5

Tool names use a mix of patterns (verb_noun, noun_noun, adjective_noun) but are consistently lowercase with underscores. Some names are vague like 'forever' and 'recent_alerts', but overall readable.

Tool Count3/5

32 tools is on the high side for a single server, but many are specialized and serve a broad data query platform. Some tools are meta-tools covering multiple use cases, which could reduce the need for so many.

Completeness4/5

The tool set covers a wide range of functionalities including data querying, entity resolution, comparisons, verification, memory, subscriptions, and prediction markets. Minor gaps like data export are not critical for its purpose.