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MCP Server for Splunk

MCP Server for Splunk

by deslicer
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Get Dashboard Definition

get_dashboard_definition

Retrieve the raw definition of a Splunk dashboard, including source (Simple XML or JSON), type, app context, owner, and viewing URL. Specify dashboard name, app, and owner to get complete data for inspection or troubleshooting.

Instructions

Get the raw definition of a specific dashboard. Returns the complete dashboard source (Simple XML or Dashboard Studio JSON), type, app context, owner, and Splunk Web viewing URL.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "name": "<name>"
    },
    "name": "get_dashboard_definition"
  }
}

Input Schema

NameRequiredDescriptionDefault
appNoApp context. Default: 'search'search
nameYesDashboard name (required)
ownerNoDashboard owner. Default: 'nobody'nobody

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv0.6.1
    • addedInput schema / properties / app / description
      Added value: +"App context. Default: 'search'"
    • addedInput schema / properties / name / description
      Added value: +"Dashboard name (required)"
    • addedInput schema / properties / owner / description
      Added value: +"Dashboard owner. Default: 'nobody'"
  2. Changed1 schema field changedv0.5.1
    • addedInput schema / additionalProperties
      Added value: +false
  3. First observedv0.5.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the burden of behavioral disclosure. It explicitly states what is returned: the complete dashboard source (Simple XML or Dashboard Studio JSON), type, app context, owner, and viewing URL. It does not mention permissions or error behavior, but it provides sufficient context about the operation's behavior.

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 two sentences long, front-loaded with the core purpose, and every sentence adds useful information. There is no redundancy or filler.

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?

This is a simple read tool with three fully documented parameters and no output schema. The description compensates well by listing the return contents and the resource type. It could note behavior on missing dashboards or permission requirements, but the current level is adequate for the tool's low complexity.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents all three parameters and their defaults. The description adds minimal parameter-specific meaning, only reinforcing that it operates on a specific dashboard by name, which aligns with the baseline of 3.

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 uses a specific verb and resource: "Get the raw definition of a specific dashboard," clearly distinguishing this from list_dashboards and create_dashboard. It also enumerates the returned data, making the tool's function unmistakable.

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 implies this is for retrieving the raw definition of a specific dashboard rather than listing dashboards, which contrasts with the sibling list_dashboards tool. It does not explicitly list alternatives or exclusions, but the context is clear enough for an agent to select it appropriately.

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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