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list_datasets

Coverage and status of every dataset: status, row count, source instrument, effective date, publisher, official source URL, last check, data period, cadence, maximum age, next expected update, and the catalogue page. The licence, caveat and fallback fields are Indonesian text.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, and it does disclose the shape of each returned record plus a useful language note that licence, caveat and fallback fields are Indonesian text. But it says nothing about access requirements, result size/ordering, caching, or refresh semantics, which are the remaining behavioral gaps for an unannotated catalogue tool.

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?

A single dense sentence with zero filler, front-loaded on the core purpose with the field inventory following. The long comma-separated field list is slightly unwieldy to scan but every item is substantive, and the separate language caveat sentence earns its place.

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?

There is no output schema, so the field enumeration is doing real work in telling the agent what comes back, and 'every dataset' answers the completeness question. The main shortfall is the missing usage/routing context, which for a simple no-arg catalogue tool is a modest rather than critical gap.

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 takes zero parameters, so there is no parameter semantics to document and the baseline of 4 applies. The description correctly does not invent parameters and instead spends its words on output content.

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 names the resource (datasets) and enumerates the exact fields each entry carries (status, row count, source instrument, effective date, publisher, URL, cadence, next expected update, catalogue page), which is far more specific than a bare 'list' tautology. However, it never states the verb ('list all datasets') explicitly and doesn't differentiate itself from siblings like list_stat_indicators or list_food_commodities, so an agent must infer the scope boundary.

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?

There is no when-to-use guidance at all: nothing says this is a discovery/catalogue tool to consult before calling get_stat_series or get_kbli, nor any exclusion against sibling list_* tools. The agent is left to guess whether this is a prerequisite step or an end in itself.

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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