Mostly Right

List a dataset's tables

list_tables

Read-onlyIdempotent

The tables in one dataset, without the full column schemas — the cheap call when you only need table ids and titles. Example: {"dataset_slug": "kden-metar-hourly"}. Returns {dataset_slug, main_table, tables: [{id, slug, title, display_title?, description?, section?, license, version_id, capabilities}]} in curated order. section contains id, title and collapsed; collapsed is only a UI preference, not an access restriction. Use get_dataset instead when you also want columns.

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_slug": "<dataset_slug>"
    },
    "name": "list_tables"
  }
}

Input Schema

Name	Required	Description	Default
`dataset_slug`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 10, 2026

TDQS

A4.6/5.0

Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the description doesn't need to restate it. It adds real behavioral context instead: result ordering ("curated order") and a clarification that section.collapsed is only a UI preference and not an access restriction — a detail an agent could otherwise misread as a permission gate.

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?

Front-loads the purpose and cost framing, then the example, then the return shape, then the sibling routing — a sensible order. The inline return-shape blob is dense but every element (section, collapsed) is functional; only minor tightening is possible.

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

Completeness5/5

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

No output schema exists, and the description supplies the full return shape including optional fields and a caveat about UI-only flags. For a single-parameter read tool with annotations covering safety, nothing an agent needs to call or interpret the result is missing.

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?

Schema description coverage is 0%, so the single parameter is undocumented structurally; the description compensates with a concrete example object showing the expected slug format ("kden-metar-hourly"). It does not state where the slug comes from or how to resolve one, which keeps it from a 5.

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?

States a concrete resource and scope ("The tables in one dataset") and immediately bounds it ("without the full column schemas"), which distinguishes it from schema-returning siblings. The contrast with get_dataset makes the distinction unambiguous without opening either schema.

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?

Explicitly frames the call as "the cheap call when you only need table ids and titles" and names the alternative with its selection condition: "Use get_dataset instead when you also want columns." Both the when and the when-not are 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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