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eea-climate-data-mcp

eea-climate-data-mcp

by bezilaszlo
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

search_tables

Locate climate datasets by searching table names and column descriptions in the EEA Discodata service.

Instructions

Search table names, descriptions, columns, and column descriptions.

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": {
      "text": "<text>"
    },
    "name": "search_tables"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden for behavioral disclosure. It only lists searchable fields and does not mention return format, case sensitivity, read-only nature, or any limitations. This minimal disclosure is inadequate for a complete behavioral understanding.

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 a single, focused sentence that succinctly states what the tool does without unnecessary words or repetition. It is appropriately sized for the tool's simplicity.

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

Completeness3/5

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

Given the simple single-parameter input and presence of an output schema, the description meets minimum viability. However, without annotations, it could provide more context about what results are returned (e.g., matching table names or full metadata) and how the search behaves, leaving some gaps.

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?

The schema provides no description for the 'text' parameter (0% coverage). The description implies that 'text' is the search term applied across the listed fields, but does not specify matching semantics such as substring vs exact match. This adds some meaning beyond the schema but leaves ambiguity.

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 identifies the tool as a search operation over table metadata (names, descriptions, columns, and column descriptions). This specific verb+resource combination distinguishes it from sibling tools like list_tables (which lists all tables) and describe_table (which describes a specific table).

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 finding tables based on keyword search, but does not explicitly state when to use this tool versus alternatives. No exclusions or alternative guidance is provided, so the usage context is implied rather than stated.

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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