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list_datasets

List datasets of a project (compact: title, kind, unit, summary)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
projectIdYesProject ID

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
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 of behavioral disclosure. It communicates a read-only listing behavior and specifies the compact output fields (title, kind, unit, summary), which is useful. However, it does not mention pagination, ordering, whether all datasets are returned, or what happens when a project has no datasets.

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?

The description is a single compact sentence that front-loads the verb, resource, and scope, then adds the output summary in parentheses. There is no redundant or filler content.

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 one-parameter list tool with no output schema, the description is largely complete: it names the required input scope and the output fields. It could be marginally improved by noting that more detailed dataset information is available via get_dataset, but nothing essential for invoking the tool correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with projectId described as 'Project ID'. The description does not add extra meaning beyond the schema, which is acceptable given the schema is already complete and the single parameter is self-explanatory. Baseline 3 applies.

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 a specific action ('List datasets') and a specific scope ('of a project'), with a compact output format. This distinguishes it from sibling tools like get_dataset (singular dataset detail) and save_dataset (write operation). The purpose is immediately unambiguous.

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 gives clear context: use this to list datasets within a project. It does not explicitly state when not to use it or mention alternatives, but the context strongly implies the appropriate use case. A named alternative such as get_dataset for full details would make it stronger.

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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