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eea-climate-data-mcp

eea-climate-data-mcp

by bezilaszlo
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

get_measure

Fetch a single climate policy or measure with its quantification block: reporting status, ex-ante values by scope and target year, and ex-post average if reported. Get full EEA record details.

Instructions

Fetch one policy or measure with explicit quantification semantics.

Returns the full record plus a quantification block: status is 'reported' or 'not_reported'; ex-ante values by scope (total/esr/ eu_ets/lulucf) and target year, ex-post average if reported.

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": {
      "country": "<country>",
      "measure_id": "<measure_id>"
    },
    "name": "get_measure"
  }
}

Input Schema

NameRequiredDescriptionDefault
countryYes
measure_idYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.9/5.0
Behavior4/5

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

The description discloses the return structure in detail: status values ('reported'/'not_reported'), ex-ante values by scope, and ex-post average if reported. With no annotations provided, this gives useful transparency about quantification semantics.

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?

Two sentences; the first clearly states the purpose, the second provides necessary detail. No fluff.

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 simple 2-parameter tool and presence of an output schema, the description covers the core functionality and return semantics, making it sufficiently complete for a fetch operation.

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 coverage is 0% and the description does not elaborate on the parameters beyond their names; no format or validation details are given.

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 states 'Fetch one policy or measure with explicit quantification semantics' – a specific verb+resource, and the phrase 'one' clearly differentiates this from sibling tools like list_measures.

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 implies usage for retrieving a single measure via country and measure_id but does not explicitly mention alternatives or provide when/when-not guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

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