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deslicer

MCP Server for Splunk

by deslicer
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Python
Hybrid

workflow_runner

Execute workflows by ID with parallel processing and parameter control for efficient troubleshooting in Splunk.

Instructions

Execute any available workflow by ID with comprehensive parameter control and parallel execution.

This tool provides a flexible interface to execute both core (built-in) and contrib (user-contributed) workflows with full control over execution parameters and diagnostic context. It leverages the same parallel execution engine used by the dynamic troubleshoot agent for optimal performance.

Core Capabilities:

  • Universal Execution: Run any workflow by ID - core or contrib workflows

  • Parameter Flexibility: Full control over time ranges, focus areas, and complexity levels

  • Parallel Processing: Dependency-aware parallel task execution for optimal performance

  • Comprehensive Results: Detailed execution results with performance metrics and summaries

  • Progress Tracking: Real-time progress reporting during workflow execution

Key Parameters:

  • workflow_id (required): ID of workflow to execute (use list_workflows to discover)

  • problem_description (optional): Context about the specific issue being investigated

  • earliest_time/latest_time (optional): Time range for diagnostic searches (default: "-24h" to "now")

  • focus_index/focus_host/focus_sourcetype (optional): Specific focus areas for targeted analysis

  • complexity_level (optional): "basic", "moderate", "advanced" analysis depth (default: "moderate")

  • enable_summarization (optional): AI-powered result summarization (default: True)

Supported Workflows:

  • Core Workflows: missing_data_troubleshooting, performance_analysis

  • Contrib Workflows: Any custom workflows from contrib/workflows/ directory

Benefits:

  • Consistent interface for all workflow types

  • Optimized parallel execution with dependency management

  • Flexible parameter control for different scenarios

  • Comprehensive result analysis and reporting

  • Integration with existing workflow infrastructure

When to use

  • Use when you know the workflow ID to run (discover via list_workflows)

  • Use for executing core or contrib workflows with custom time windows and focus context

  • Use in automation pipelines that orchestrate troubleshooting by workflow ID

Arguments

  • See Key Parameters list above. All are optional except workflow_id.

Outputs

  • Detailed execution results, task results, summary, and metadata including execution timing

Perfect for executing specific workflows when you know exactly which diagnostic procedure you need to run, or for building automated troubleshooting pipelines.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
workflow_idYes
problem_descriptionNo
earliest_timeNo-24h
latest_timeNonow
focus_indexNo
focus_hostNo
focus_sourcetypeNo
complexity_levelNomoderate
enable_summarizationNo

Tool Definition Quality

A4.5/5.0
Behavior4/5

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

No annotations are provided, so the description bears full burden. It discloses parallel processing, progress tracking, and comprehensive results. It does not mention side effects or rate limits, but the behavioral traits are well explained.

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 sections and bullet points, and is front-loaded with a summary. However, it is somewhat verbose (e.g., 'Benefits' section), but still efficient.

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

Completeness5/5

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

With 9 parameters, no output schema, and no annotations, the description comprehensively covers purpose, parameter details, supported workflows, outputs (execution results, metrics), and integration hints. It leaves little ambiguity.

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 coverage is 0%, but the description provides a detailed 'Key Parameters' section explaining each parameter's purpose, defaults, and options (e.g., complexity_level values). This fully compensates for the lack of schema 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 any available workflow by ID' and details core capabilities like universal execution and parameter flexibility. It distinguishes from sibling tools like list_workflows and workflow_builder by specifying workflow ID usage and referencing list_workflows for discovery.

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 explicitly says 'Use when you know the workflow ID to run' and provides context for automation. It lacks explicit when-not-to-use or alternative suggestions, but the guidance is clear and helpful.

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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