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deslicer

MCP Server for Splunk

list_dashboards

Retrieve dashboards from Splunk with metadata including type, app, owner, permissions, and viewing URLs. Filter by owner, app, type, or search term.

Instructions

List dashboards in Splunk (Simple XML and Dashboard Studio). Returns metadata including name, label, type (classic/studio), app, owner, permissions, sharing level, last updated, and Splunk Web viewing URLs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ownerNoFilter by owner. Use 'me' for current user's dashboards, 'nobody' for shared dashboards, or a specific username. Default: 'nobody'nobody
appNoFilter by app context. Default: '-' (all apps)-
countNoMax results to return. 0=all, default: 50 for performance
offsetNoResult offset for pagination. Default: 0
search_filterNoFilter results (e.g., 'name=*security*')
type_filterNoFilter by type: 'classic', 'studio', or 'any'. Default: 'any'any
my_dashboards_onlyNoIf True, only return dashboards owned by the current user. Overrides 'owner' parameter. Default: False
private_onlyNoIf True, only return private dashboards (sharing='user'). Works with any owner filter. Default: False
Behavior2/5

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

With no annotations provided, the description must disclose behavioral traits but only mentions returns. It hints at performance via the count parameter's default but does not discuss side effects, authentication needs, rate limits, or pagination behavior. Transparency is minimal beyond the return structure.

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 single sentence that lists return fields, but it could benefit from front-loading the essential purpose and structuring with bullets or clearer separation. It is not verbose, earning a 4.

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 8 parameters and no output schema, the description covers the return metadata but omits pagination details (e.g., how offset/count affect multiple pages) and error scenarios. It is adequate for basic use but leaves gaps for comprehensive understanding.

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%, and the description adds context about listing dashboards and return fields. However, it does not significantly enhance parameter meaning beyond what the schema already provides (e.g., 'owner' default explanation). Baseline 3 is appropriate as the description adds some but limited value.

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 explicitly states it lists dashboards in Splunk, distinguishing between Simple XML and Dashboard Studio types, and enumerates the returned metadata fields. This provides clear purpose and distinguishes from sibling tools like get_dashboard_definition or create_dashboard.

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

Usage Guidelines2/5

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

The description offers no guidance on when to use this tool versus alternatives, such as get_dashboard_definition for a single dashboard or other list tools. It lacks when-not or context for filtering, leaving the agent to infer usage without explicit direction.

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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