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Api Fx Rates

api_fx_rates

ECB reference currency exchange rates. ?base=USD&symbols=EUR,GBP or ?date=2026-01-15 [HTTP x402 price: $0.002]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. Changed2 schema fields changed
    • removedInput schema / properties / params / additionalProperties
      Removed value: -true
    • addedInput schema / properties / params / properties
      Added value: +{
      +  "base": {
      +    "anyOf": [
      +      {
      +        "type": "string"
      +      },
      +      {
      +        "type": "null"
      +      }
      +    ],
      +    "default": null
      +  },
      +  "symbols": {
      +    "anyOf": [
      +      {
      +        "type": "string"
      +      },
      +      {
      +        "type": "null"
      +      }
      +    ],
      +    "default": null
      +  }
      +}
  2. First observed

TDQS

B3/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full behavioral burden. It reveals the data source (ECB) and hints at a paid HTTP call via '[HTTP x402 price: $0.002]', but it does not explicitly state that this is a network fetch, mention rate limits or failure modes, or disclose what happens for invalid dates.

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 extremely compact: one clear subject phrase, two illustrative query examples, and a cost hint. The core purpose is front-loaded and there is no filler text.

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

Completeness2/5

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

Despite having an output schema, the description is incomplete for a tool with nested parameters. It does not state default behavior (e.g., latest rates when no date is given), does not clarify whether date can be combined with base/symbols, and omits the date parameter from the schema entirely. An agent cannot safely determine all valid invocation patterns.

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?

The schema has 0% description coverage, so the examples are the only explanation of parameters. They partially clarify 'base' and 'symbols' (currency codes, comma-separated), but they introduce a 'date' parameter that is absent from the params schema, creating a mismatch that could cause an agent to invoke the tool incorrectly.

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 names a specific resource ('ECB reference currency exchange rates') and domain, making it clear this tool deals with FX rates. It lacks an explicit verb like 'get' or 'fetch,' but the meaning is unambiguous and it differs from all sibling tools.

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

Usage Guidelines2/5

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

The description provides query-string examples ('?base=USD&symbols=EUR,GBP' or '?date=2026-01-15') but gives no explicit guidance on when to use this tool versus alternatives, no exclusions, and no context such as 'use for current rates' or 'use for historical rates.'

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

B3.1/5.0
Disambiguation2/5

Several tools have unclear boundaries: api_search and api_serp_google both return Google results, api_scrape and api_render_text both extract page text, and api_hash_multi overlaps with api_sha256 for SHA-256/SHA-512. While many tools are distinct, these overlapping pairs create real misselection risk.

Naming Consistency5/5

Every tool follows the same api_<snake_case> pattern with no mixed conventions or casing styles. The prefix makes the server immediately recognizable and the action/resource is consistently readable across all 44 tools.

Tool Count2/5

44 tools is well over the 25+ threshold for a well-scoped set, making the server feel like a grab-bag of unrelated utilities. Even though each tool is small and individually useful, the overall surface is too large and would benefit from consolidation into focused sub-servers.

Completeness3/5

The set covers many common utility categories—encodings, conversions, text analysis, web scraping, SEO, and trends—but has notable one-way gaps: CSV/YAML/TOML all convert to JSON but not back, and markdown converts to HTML but not the reverse. The broad domain makes full completeness hard to define, so only major reverse-conversion gaps stand out.