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import_data_table_csv

Destructive

Bulk-load a DataTable from a CSV string to author or restore many rows at once. It replaces existing rows, so check problem_count and problems for per-cell failures.

Instructions

Bulk-load a DataTable from a CSV string -- the fast path for authoring or restoring many rows at once. DESTRUCTIVE: it REPLACES the table's rows, so any row not in the CSV is gone; export first (export_data_table_as_csv) if you meant to merge. Always read the problems array and problem_count: the native importer reports per-cell failures there while still succeeding overall, and enum values whose display name contains spaces are cut at the first space and silently land on the index-0 enumerator. For a handful of rows prefer set_data_table_rows_from_json, which reports per-row results.

Returns: { table_path, row_struct, rows_imported, row_count, problem_count, saved, problems, row_names }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
csv_stringYesCSV content (header row + one row per data row)
table_pathYesFull asset path of the DataTable

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare destructiveHint=true and readOnlyHint=false, but the description goes further by explaining exactly what is destroyed ('REPLACES the table's rows, so any row not in the CSV is gone') and by disclosing two silent failure modes: per-cell problems still succeed overall, and enum display names with spaces are truncated and land on index-0. It also lists the return fields, which matters because no output schema exists.

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 front-loaded with purpose and the DESTRUCTIVE warning, then covers failure handling, alternatives, and return shape. Every sentence carries decision-relevant information, and the 'Returns:' line compensates for the missing output schema without padding.

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 destructive two-parameter bulk import with no output schema, the description supplies the missing return shape, the destructive semantics, and the silent failure modes an agent must check. Nothing needed to invoke it safely and interpret the result is absent.

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%, so both parameters are already documented in the schema. The description adds no syntax, format, or constraint details beyond what the schema provides for csv_string and table_path, so the baseline of 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 states a specific verb+resource: 'Bulk-load a DataTable from a CSV string,' and explicitly frames it as 'the fast path for authoring or restoring many rows at once.' It also names the sibling it is not (set_data_table_rows_from_json) and the export counterpart, so an agent can distinguish it without opening another schema.

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

Usage Guidelines5/5

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

Explicit when-to-use (bulk authoring/restoring many rows), when-not-to-use (if you meant to merge, export first), and alternative (for a handful of rows prefer set_data_table_rows_from_json). This covers both the destructive replacement case and the small-batch case, leaving little to inference.

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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