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Import CSV

import_csv

Turn CSV rows into LightCMS content pages by setting the title column; every column becomes a page field.

Instructions

Import content from CSV data. Each row becomes a content page. Specify which column is the title; all columns become content fields.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
csv_dataYesRaw CSV text (first row must be headers),required
folder_pathNoFolder path for imported pages (default /imports)
slug_columnNoHeader name of the column to use as the URL slug (defaults to slugified title)
auto_publishNoAutomatically publish imported pages (default false)
title_columnYesHeader name of the column to use as the page title,required
template_nameNoTemplate name for imported pages

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv7.3.4

TDQS

B3.2/5.0
Behavior3/5

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

The annotations already declare destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description usefully adds the row-to-page mapping and column-to-field behavior, but says nothing about whether the import is synchronous or spawns a job (relevant given siblings like get_import_job and cancel_import_job), nor about permissions or limits.

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

Conciseness4/5

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

Three short sentences, front-loaded with the core operation and each sentence adding information (transformation model, title column, field mapping). No filler or repetition of the title.

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

Completeness3/5

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

For a mutation tool with no output schema, the description covers the essential mechanics but omits whether the result is a job, what happens on partial failure, and where pages land by default. Annotations carry the safety burden, but the operational picture is incomplete.

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 the schema documents all six parameters, making the baseline 3 appropriate. The description adds the semantic that "all columns become content fields" and how the title column is chosen, which is slightly beyond the schema but not deep parameter guidance.

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?

States a specific verb and resource ("Import content from CSV data") and clarifies the transformation ("Each row becomes a content page"), so the agent grasps the operation immediately. It does not differentiate itself from close siblings like import_markdown or bulk_create_content, which is the only gap.

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

Usage Guidelines2/5

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

The description explains what the tool does but never says when to use it versus alternatives such as import_markdown, bulk_create_content, or create_import_source. There are no exclusions, prerequisites, or selection criteria, leaving routing entirely 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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