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ecb-mcp

ecb-mcp

by scka-de
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

explain_dataset

Understand ECB dataset structure by exploring dimensions, valid values, and example queries to formulate accurate data requests.

Instructions

Explain the structure of any ECB dataset — dimensions, valid values, and example queries.

Use this after search_datasets to understand what a dataset contains and how to query it. Returns the dataset's dimensions, their valid values (codes), and an example series key. Works for all 100+ ECB datasets, not just the ones with dedicated tools.

Examples of questions this tool answers:

  • "What dimensions does the EXR dataset have?"

  • "What are the valid currency codes in the exchange rate dataset?"

  • "How do I query the BSI (balance sheet) dataset?"

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "dataset_id": "<dataset_id>"
    },
    "name": "explain_dataset"
  }
}

Input Schema

NameRequiredDescriptionDefault
dataset_idYesDataset ID (e.g. "EXR", "FM", "ICP", "YC", "BSI"). Use search_datasets to find IDs.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A4.1/5.0
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 burden. It describes what the tool returns ('dimensions, valid values, example series key') and its scope ('all 100+ ECB datasets'), but lacks details on error handling, rate limits, or authentication requirements. The behavioral disclosure is adequate but not comprehensive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded with the core purpose in the first sentence. The bulleted examples are helpful but slightly verbose. Overall, it's efficient with little waste, though it could be slightly more streamlined.

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?

Given the tool's moderate complexity (1 parameter, no output schema, no annotations), the description is mostly complete. It covers purpose, usage, and output content well, but lacks details on behavioral aspects like errors or limits. The absence of an output schema is partially compensated by describing return values.

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 the parameter 'dataset_id' well-documented in the schema. The description adds minimal value beyond the schema by mentioning example IDs (e.g., 'EXR', 'FM') and referencing search_datasets for discovery, but doesn't provide additional syntax or format details. Baseline 3 is appropriate given high schema coverage.

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's purpose with specific verbs ('explain the structure', 'understand what a dataset contains') and resources ('ECB dataset', 'dimensions, valid values, example queries'). It distinguishes from sibling tools by focusing on dataset metadata rather than data retrieval or conversion.

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?

The description explicitly states when to use this tool ('Use this after search_datasets to understand what a dataset contains') and provides clear alternatives by mentioning sibling tools in examples (e.g., 'EXR dataset' relates to get_exchange_rates). It also clarifies scope ('Works for all 100+ ECB datasets, not just the ones with dedicated tools').

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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