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list_datasets

Retrieve an inventory of in-memory datasets with their names, types, shapes, memory use, parent links, and provenance to identify and inspect available data.

Instructions

List datasets currently held in memory: name, kind (table|tensor), shape, memory, parent, how they were made.

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

Name	Required	Description	Default
No arguments

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.1Nov 9, 2026

TDQS

A3.9/5.0

Behavior3/5

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

With no annotations, the description carries the full burden; "List" plus the in-memory scope implies a side-effect-free read, and the field list hints at what each row contains. It does not state ordering, whether entries are snapshots, or that nothing is mutated or evicted, so the safety profile is only inferable.

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 front-loaded sentence with the scope stated before the field enumeration; nothing is padded. The one weakness is that "how they were made" is prose where the other items are concrete field names, slightly blurring the return-value summary.

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?

An output schema already defines the return shape, so the description only needs to convey scope and intent, which it does. For a zero-argument read tool this is nearly sufficient; ordering of results and whether the listing reflects live state are the only unstated details.

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 spends no words on arguments.

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 specific verb and resource ("List datasets") scoped to "currently held in memory", and enumerates the returned fields (name, kind, shape, memory, parent, provenance). This is unmistakably different from siblings like load_dataset, drop_dataset, or describe, which mutate or inspect a single object.

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 by the name and by "currently held in memory", so an agent can infer it is the enumeration entry point. However, it never says when to reach for this versus describe or plot, nor any prerequisite such as needing datasets loaded first.

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