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List Datasets

list_datasets

Lists all datasets currently available in the session with their metadata, so you can review what data is loaded before inspecting, cleaning, or modeling it.

Instructions

List all datasets currently available in the session. Returns: dict: {"datasets": [metadata of every dataset]}.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_datasets"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden, and it does disclose the return shape (a dict keyed "datasets" containing metadata for every dataset). It says nothing about ordering, whether the list is empty when nothing is loaded, or any permission/session preconditions, so behavioral coverage is partial rather than complete.

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?

Two short lines with the action front-loaded and the return shape immediately after; nothing is padded. The "Returns: dict:" block is slightly code-doc-formatted rather than prose, but it is still easy to scan.

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?

This is a simple zero-parameter listing tool with no output schema, and the description supplies both the scope (session) and the return envelope, which is enough for correct invocation. Only edge-case behavior (empty session, ordering of results) is left unstated, a minor gap for this complexity.

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, which is the baseline-4 case; there are no ambiguities for the description to resolve. The description consistently implies a no-argument, session-wide call, matching the empty schema.

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 uses a specific verb and resource ("List all datasets currently available in the session") and states the scope as session-level. It does not distinguish itself from near-neighbors like dataset_info or list_columns, which also expose dataset-level information, so an agent must infer the difference from names alone.

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?

Usage is implied rather than stated: for a zero-parameter enumeration tool, the use case (discovering what is loaded before calling analysis tools) is fairly obvious from the description. However, it never explicitly says when to call this versus dataset_info or describe_dataset, nor does it set any exclusions.

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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