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Bulk Create

bulk_create
Destructive

Upload a CSV of image URLs to queue batch alt-text generation, with optional email notification for completion.

Instructions

Upload a local CSV file from the MCP server machine and queue asynchronous alt-text generation using account credits. The response does not confirm generation completion. CSV should have columns: url (required), asset_id, lang, keywords, tags, metadata (optional).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
emailNoEmail for completion notification
csv_fileYesPath to CSV file with image URLs and optional metadata

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.0.5
    • changedInput schema / properties / email / pattern
      Previous value: -"^(?!\\.)(?!.*\\.\\.)([A-Za-z0-9_'+\\-\\.]*)[A-Za-z0-9_+-]@([A-Za-z0-9][A-Za-z0-9\\-]*\\.)+[A-Za-z]{2,}$"New value: +"^(?:[A-Za-z0-9_'+\\-]+\\.)*[A-Za-z0-9_'+\\-]*[A-Za-z0-9_+-]@(?:[A-Za-z0-9][A-Za-z0-9\\-]*\\.)+[A-Za-z]{2,}$"
  2. First observedv1.0.4

TDQS

A4.7/5.0
Behavior5/5

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

Adds significant behavioral context beyond annotations: the response does not confirm generation completion, the operation is asynchronous, and it consumes account credits. Also clarifies the CSV file must exist on the MCP server machine. Consistent with destructiveHint=true and readOnlyHint=false.

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?

Three sentences, each earning its place: the first states the core operation, the second warns about the async response behavior, and the third specifies the CSV format. No fluff, front-loaded with the main action.

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?

Complete for a bulk async tool with a 2-param schema and annotations covering safety. The only gap is that no output schema exists and the description does not indicate what the response actually contains (e.g., a request ID) beyond stating it does not confirm completion. Minor, given the async nature.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although schema coverage is 100%, the description adds essential semantics not present in the schema: the exact CSV column structure (url required; asset_id, lang, keywords, tags, metadata optional). This goes beyond the schema's generic 'Path to CSV file with image URLs and optional metadata' and is critical for correct invocation.

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 action (upload local CSV, queue async alt-text generation) with a clear resource (CSV file) and scope (bulk, uses account credits). It distinguishes itself from siblings like generate_alt_text and generate_alt_text_from_file by emphasizing the bulk, asynchronous, local-file nature.

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?

Provides clear context for when to use the tool: when you have a local CSV on the MCP server machine and want asynchronous bulk generation. However, it does not explicitly name alternatives or state exclusions, leaving the agent to infer the distinction from the surrounding text.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.