Append rows of data to an existing dataset. The schema is automatically inferred from the first batch. All values are stored as text. Maximum 10,000 rows per call; use multiple calls for larger datasets. Requires AUTARIO_API_KEY.
| Name | Required | Description | Default |
|---|---|---|---|
| rows | Yes | Array of row objects where keys are column names (e.g. [{"country": "USA", "year": "2024", "value": "25000"}]) | |
| dataset_id | Yes | The UUID of the dataset to append rows to |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Adds beyond annotations: all values stored as text, max rows per call, and auth requirement. No contradiction with annotations (readOnlyHint=false, destructiveHint=false).
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?
Four concise sentences, front-loaded with purpose, no wasted words. Each sentence adds critical 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?
Covers purpose, schema inference, value type, row limit, and auth. Despite no output schema, key behavioral aspects are addressed. Lacks details on error handling or what happens if dataset missing, but sufficient for typical use.
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?
Schema coverage is 100%, but description adds 'all values stored as text' and that schema is inferred from first batch, which adds value beyond the schema's parameter descriptions.
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?
Clear verb 'append' and resource 'rows to an existing dataset'. Distinguishes from siblings like 'clear_rows' and 'create_dataset'.
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?
Explicitly states max rows (10,000) and suggests multiple calls for larger data. Mentions API key requirement and automatic schema inference, guiding usage. Does not explicitly mention alternatives but implies them.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools are strongly domain-specific with clear boundaries, especially the 360 reports and dataset/chart CRUD tools. Some overlap exists around driver analysis (find_drivers, what_matters, decompose_drivers) and dataset discovery (search_datasets, discover_by_topic, list_indicators), but the descriptions make the intended use cases mostly distinguishable.
The vast majority of tools follow a clear snake_case verb_noun or get_noun pattern, e.g. list_connectors, refresh_connector, query_dataset, delete_dataset. Minor deviations such as calculate, describe, bubble_or_not, what_matters, and the 360-style report names keep it from being perfectly uniform.
48 tools is far beyond the 3-15 range and even beyond the 25-tool threshold for a heavy surface. The platform is broad and the tools are organized into domains, but the sheer number creates a high selection burden for an agent and suggests the server is trying to cover too many workflows in one toolset.
The toolset covers dataset lifecycle, chart lifecycle, data discovery, querying, statistics, app context, connectors, and admin reports remarkably well. Notable gaps are the lack of a delete_chart tool and no row-level update/delete for datasets, but agents can generally work around these or treat them as intentional platform constraints.
