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Autario

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by Autario
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JavaScript
Hybrid

write_rows

Append up to 10,000 rows to an existing Autario dataset, automatically inferring the schema from the first batch so you can add computed, scraped, or sourced data.

Instructions

Append rows of data to an existing dataset you own (from create_dataset) | your own numbers, a derived table you computed, or a series you scraped together from World Bank / FRED / Eurostat results. 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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rowsYesArray of row objects where keys are column names (e.g. [{"country": "USA", "year": "2024", "value": "25000"}])
dataset_idYesThe UUID of the dataset to append rows to

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.1.0

TDQS

A4.7/5.0
Behavior5/5

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

Annotations provide minimal behavioral signal, so the description carries the burden. It discloses schema auto-inference, text-only storage, the 10,000-row limit with a multi-call strategy, and the AUTARIO_API_KEY requirement.

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?

A single dense sentence front-loads the core action and then packs only high-value constraints and requirements. No filler words or redundant restatement of the schema.

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

Completeness5/5

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

For a low-complexity, two-parameter write tool with no output schema, the description covers ownership, allowed data sources, limits, storage behavior, and authentication. Nothing essential is missing for the agent to call it correctly.

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 schema already fully documents both parameters with an example. The description adds useful semantic context by explaining that values are stored as text and the schema is inferred from the first batch.

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 operation, append rows, to a specific resource, an existing dataset you own, and clarifies origin via create_dataset. This distinguishes it from sibling create_dataset and clear_rows.

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?

Clearly indicates when to use the tool: to append data to a dataset you own, with examples of acceptable data sources. It does not explicitly contrast with alternatives like clear_rows, but the usage context is unambiguous.

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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