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prompt_from_file2file_tool

Process prompts from files through multiple LLM models and save responses to files for comparison and analysis.

Instructions

Read a prompt from a file, send it to multiple LLM models, and write responses to files.

Args:
    file_path: Path to the file containing the prompt text
    models_prefixed_by_provider: List of models in format "provider:model" (e.g., "openai:gpt-4").
                                 If None, defaults to ["openai:gpt-4o-mini"]
    output_dir: Directory where response files should be saved (defaults to input file's directory/responses)
    output_extension: File extension for output files (e.g., 'py', 'txt', 'md')
                      If None, defaults to 'md' (default: None)
    output_path: Optional full output path with filename. If provided, the extension
                 from this path will be used (overrides output_extension).

Returns:
    List of file paths where responses were written

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
file_pathYes
models_prefixed_by_providerNo
output_dirNo
output_extensionNo
output_pathNo
Behavior2/5

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

With no annotations provided, the description carries full burden but only covers basic operations. It doesn't disclose critical behavioral traits such as error handling, rate limits, authentication needs, file format requirements, or whether it overwrites existing files. The description adds minimal context beyond the core functionality.

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 well-structured with a clear purpose statement followed by parameter details. It's appropriately sized for a 5-parameter tool, though the parameter explanations could be more front-loaded. Every sentence adds value, with minimal redundancy.

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?

Given the tool's complexity (multi-model processing, file I/O) and lack of annotations/output schema, the description is moderately complete. It covers core functionality and parameters but omits important context like error behavior, response formats, or performance characteristics. The return value is documented, but overall completeness is adequate with noticeable gaps.

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%, so the description must compensate. It provides meaningful explanations for all 5 parameters, clarifying formats (e.g., 'provider:model'), defaults, and interactions (e.g., output_path overrides output_extension). This adds substantial value beyond the bare schema, though some details like file path validation are missing.

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 tool's purpose with specific verbs ('read', 'send', 'write') and resources ('prompt from a file', 'multiple LLM models', 'responses to files'). It distinguishes itself from sibling tools like 'prompt_from_file_tool' by specifying multi-model processing and file output, avoiding tautology.

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

Usage Guidelines3/5

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

The description implies usage for batch processing prompts through multiple models, but lacks explicit guidance on when to use this tool versus alternatives like 'prompt_tool' or 'prompt_from_file_tool'. No exclusions or prerequisites are mentioned, leaving usage context partially unclear.

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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