list_datasetsRetrieve an account's datasets, newest first, including IDs, names, column counts, row counts, and newest run details for selecting data to enrich or analyze.
list datasets
The account's datasets, newest first (up to 200): id, name, column counts, row_count, newest run. get_dataset has the columns and enrichment.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.3.5Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, and non-destructive, so safety is covered. The description adds value the annotations do not: a hard cap of 200 results, newest-first ordering, and the exact fields returned. It does not discuss pagination beyond the cap, which is the only gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences, front-loaded with scope then routing. The leading 'list datasets' restates the title and earns little, but the rest is dense with useful information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
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 description's field list (id, name, column counts, row_count, newest run) and the 200-item cap are the only place an agent learns what comes back. Nothing needed to call this zero-arg tool correctly is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool takes zero parameters, so the baseline is 4. The description correctly spends no space on parameter semantics and instead documents the fixed output shape.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Names a specific verb and resource (list datasets) and immediately scopes it: the account's datasets, newest first, capped at 200. It also distinguishes itself from get_dataset by noting that sibling carries the columns and enrichment, so an agent can choose without opening either schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The closing sentence routes detail-seeking calls to get_dataset, which is an explicit alternative. There is no explicit 'do not use for X' beyond that, but for a zero-parameter listing tool the usage context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.