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DeepTempo

Splunk MCP for SOC Operations

by DeepTempo

export_labels_to_deeptempo

Convert harvested field labels into a DeepTempo-compatible format for training and evaluation. Save to file with summary statistics.

Instructions

Export harvested field labels in DeepTempo-compatible format.

This tool transforms field label data from harvest_labels() into a format that DeepTempo can consume for training and evaluation. The format is designed to be generic and adaptable to DeepTempo's specific needs.

Args: field_data: Output from harvest_labels() containing field metadata output_format: Format version ("deeptempo_v1" for now) save_to_file: Whether to save the export to a file file_path: Path for output file (default: ./deeptempo_labels_export.json)

Returns: Dictionary containing: - export_data: The formatted data ready for DeepTempo - file_path: Path where file was saved (if save_to_file=True) - summary: Statistics about the export

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
file_pathNo
field_dataYes
save_to_fileNo
output_formatNodeeptempo_v1

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description takes on the burden and largely succeeds: it discloses that the tool transforms data, optionally saves to a file, and returns a dictionary with export_data, file_path, and summary. It stops short of fully detailing side effects like file overwrite behavior or error conditions, but provides more than minimal transparency.

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?

The description is well-structured with a one-sentence summary followed by Args and Returns sections. It is appropriately detailed without being verbose; every sentence adds information, and the layout allows quick scanning.

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 purpose, input provenance, parameters, and return value structure. It even notes the format is 'generic and adaptable.' It does not discuss error handling, permissions, or file overwrite semantics, but given the output schema and detailed parameter documentation, it is sufficiently complete for invocation.

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?

Schema description coverage is 0%, but the description compensates excellently. Each parameter (field_data, output_format, save_to_file, file_path) receives a meaningful explanation beyond the schema's bare type/default information, including the expected value for output_format and the default file path.

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 opens with a specific verb+resource: 'Export harvested field labels in DeepTempo-compatible format.' It further clarifies the transformation from harvest_labels() output into a consumable format for DeepTempo training/evaluation, distinguishing it from siblings like harvest_labels and correlate_with_deeptempo_finding.

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 clearly states the input comes from harvest_labels() and the intended purpose ('for training and evaluation'), giving strong contextual guidance on when to use it. It does not explicitly mention when not to use it or name alternatives, so it stops short of a 5.

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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