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queue

Execute multiple TeXFlow document operations in a single call, running them sequentially and writing the final result to disk once. Reduce round-trips by batching create, edit, layout, render, and reference actions.

Instructions

Execute multiple operations in a single call.

Each operation is a dict with 'tool' (document, layout, edit, render, reference) plus the arguments for that tool. Operations run sequentially; disk is written once at the end.

Example: queue(operations=[ {"tool": "document", "action": "create", "title": "My Doc"}, {"tool": "edit", "action": "insert", "block_type": "section", "title": "Intro", "level": 1}, {"tool": "edit", "action": "insert", "content": "Hello world.", "section": "Intro"}, {"tool": "layout", "columns": 2, "font": "palatino"} ])

Args: operations: List of operation dicts. continue_on_error: If False (default), stop on first error.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
operationsYes
continue_on_errorNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

Without annotations, the description must disclose behavior. It mentions sequential execution, single disk write, and error stopping. But it does not cover rollback, side effects, or partial execution details, leaving some behavioral gaps.

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?

The description is concise with a clear front-loaded purpose, followed by behavioral details and a well-structured example. No extraneous information.

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?

Given that an output schema exists (context signal), the description does not need to cover return values. It sufficiently explains the two input parameters and the batching concept, making it complete for a tool of moderate complexity.

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?

Schema description coverage is 0%, but the description adds significant meaning: it provides an example for the operations parameter explaining the dict structure, and clarifies continue_on_error with a default behavior. This compensates well for the schema's lack of descriptions.

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 'Execute multiple operations in a single call' and provides a detailed example showing how to batch operations from sibling tools. It distinguishes itself by acting as a batching mechanism, not a specific tool.

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?

The description explains that operations run sequentially and disk is written once at the end, implying efficiency for multiple related tasks. It also describes the continue_on_error parameter. However, it lacks explicit when-to-use or when-not-to-use guidance relative to calling individual tools.

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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