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import_glossary_csv

Import CSV files into a glossary to build custom translation memories. Supports unidirectional and multidirectional formats for flexible term management.

Instructions

Imports a CSV file into a glossary. Supports unidirectional and multidirectional formats. This is an async operation that returns an import job object containing an import_id. Poll with check_glossary_import_status using the returned import_id until the import is complete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesThe glossary ID (format: gls_*, e.g., 'gls_xyz123')
csv_contentYesThe content of the CSV file to upload
content_typeYesThe format of the CSV file. 'csv/table-uni' for unidirectional, 'csv/table-multi' for multidirectionalcsv/table-uni
gzipNoWhether the CSV content is gzip compressed

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesImport job identifier
beginNoBegin offset of the import
endNoEnd offset of the import
channelNoChannel identifier used by the import
sizeNoTotal number of entries in the import
progressNoImport progress between 0 and 1 (1 means complete)
Behavior4/5

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

Annotations indicate it's a write operation (readOnlyHint=false) and not destructive (destructiveHint=false). The description adds valuable transparency about the async nature and the need to poll with a specific tool, which is critical for correct usage.

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?

Two sentences: first states core action, second adds async details and polling instruction. Every word earns its place, with no redundancy or fluff.

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

Completeness4/5

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

The description covers the essential workflow (async, polling) and mentions format options. It could mention error handling or the gzip parameter, but the schema already describes gzip. Given the output schema exists and the sibling polling tool is named, it is sufficiently complete for an agent.

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 baseline is 3. The description adds no new information about parameters beyond what the schema provides, but it does contextualize the content_type format options (uni/multi).

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 clearly states the verb 'Imports' and resource 'CSV file into a glossary', and distinguishes from siblings by specifying CSV format and async behavior, which is unique among sibling tools.

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?

Description provides clear context for use: import CSV into glossary and poll with check_glossary_import_status. It does not explicitly exclude alternatives like import_tmx, but the context is sufficient for an agent to infer when to use this tool.

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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