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io.github.Keremozdemirra/eu-ets-mcp

io.github.Keremozdemirra/eu-ets-mcp

by Keremozdemirra
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

dataset_info

Retrieve metadata to understand the EU ETS registry dataset: snapshot date, source files, countries, activity codes, years, units, columns, withholding rules, license, and attribution.

Instructions

What the data is: registry snapshot date and retrieval time, source files with URL, SHA-256 and row counts, the countries (registry codes) and activity codes with their labels, years covered, compliance years, units with their legal definitions, the columns kept and dropped (no account-holder names), the name-withholding rule, licence (CC BY 4.0) and the attribution line to cite, and how many names are withheld and why. Cite the source line of every answer.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "dataset_info"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations, the description must carry the full behavioral burden. It thoroughly discloses the returned fields, which is valuable, but omits explicit statements about read-only safety, authentication needs, or side effects. The content itself implies a safe read, but this is not stated.

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

Conciseness3/5

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

The description is a single long run-on sentence with many clauses, making it dense and harder to parse. All content is relevant, but a bulleted structure would improve scannability and front-loading.

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 no output schema, the description comprehensively lists what is returned, covering licence, attribution, withholding rules, and source details. It is nearly complete for a no-parameter info tool, though it could explicitly state that it is a read-only metadata operation.

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?

The tool has zero parameters, so the baseline is 4. The description appropriately does not need to explain any parameter semantics, and the empty schema fully covers the absence of inputs.

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 clearly enumerates the dataset metadata returned (snapshot date, source files, codes, years, units, etc.), which distinguishes it from siblings that query installations or emitters. However, it does not explicitly state 'returns' or 'provides', relying on the 'What the data is:' framing to imply purpose.

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?

No when-to-use or when-not-to-use guidance is given. The instruction 'Cite the source line of every answer' is a post-processing rule, not a usage condition or alternative-selection cue.

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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