CatchAll

List Datasets

list_datasets

List your datasets.

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
`page`	No	Page number for pagination (default: 1).
`search`	No	Optional text filter on the dataset name.
`api_key`	No	CatchAll API key. Optional if provided via x-api-key header or CATCHALL_API_KEY env var.
`sort_by`	No	Optional sort field: 'name', 'created_at', or 'status'.
`ownership`	No	Optional ownership filter: 'all', 'own', or 'shared'.
`page_size`	No	Number of results per page (default: 100, max: 1000).
`project_id`	No	Optional filter to datasets belonging to a specific project.
`sort_order`	No	Optional sort direction: 'asc' or 'desc'.
`latest_status`	No	Optional status filter: 'pending', 'enriching', 'ready', or 'failed'.

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedJul 14, 2026

TDQS

C2.3/5.0

Behavior1/5

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

Annotations are absent, so the description must carry the full burden of behavioral disclosure. It provides no information about pagination, filtering, sorting, side effects, or default behavior. The description is nearly a tautology of the title, offering no behavioral context beyond the basic action.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

While concise, the description is under-specified. A single sentence with no structure adds no value beyond the title. For a tool with 9 parameters, more detail and structure would be beneficial to guide usage.

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

Completeness2/5

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

Given the tool's complexity (9 optional parameters) and the existence of an output schema, the description is incomplete. It does not explain how filters interact, default behavior, or what the response contains. The output schema exists but is not referenced or summarized in the description.

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 each parameter having a clear description (e.g., page, search, sort_by). The description adds nothing about parameters. Baseline 3 is appropriate because the schema fully documents parameter semantics.

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 'List' and resource 'datasets', clearly identifying the action. It distinguishes from get_dataset (singular) and list_dataset_entities by resource type, but does not explicitly contrast with sibling tools. It is clear but not fully differentiated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives like list_dataset_entities or list_projects. The description only states the action without any context on selection criteria, prerequisites, or exclusions. The agent is left to infer usage from the name alone.

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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