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List Fields

list_fields

Read-only

Lists dataset or layer fields including name, alias, type, and length. Use it to prepare SQL queries or attribute operations.

Instructions

List the fields of a dataset or layer (name, alias, type, length). Use before building SQL where-clauses or attribute operations.

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": "<dataset>"
    },
    "name": "list_fields"
  }
}

Input Schema

Name	Required	Description	Default
`dataset`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jun 14, 2026

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, so the safety profile is clear. The description adds value by specifying the returned fields (name, alias, type, length), which is useful behavioral information beyond the annotation.

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: first describes the action and output, second provides usage guidance. No redundant or unnecessary information. Front-loaded and efficient.

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?

For a simple read-only tool with an output schema and single parameter, the description covers the essential purpose, output details, and usage context. It could be improved by briefly noting error handling or data source format requirements.

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?

With 0% schema coverage, the description compensates by clarifying that the 'dataset' parameter can also refer to a layer, and explains what the tool does with it (lists fields). This adds meaningful context beyond the bare parameter name.

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 it lists fields (name, alias, type, length) of a dataset or layer, which is a specific verb+resource. It distinguishes from siblings like 'describe_data' by specifying the exact attributes returned.

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

Usage Guidelines4/5

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

The description explicitly says 'Use before building SQL where-clauses or attribute operations,' providing clear context. However, it does not mention when not to use it or suggest alternatives among siblings.

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