MCP Server for Splunk

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it returns metadata (not full definitions), supports filtering and pagination, and specifies performance defaults (e.g., count default 50). However, it lacks details on error handling, rate limits, or authentication requirements, which would elevate the score.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a clear purpose statement followed by detailed parameter explanations. Every sentence adds value, but it is moderately lengthy due to the 8 parameters. It could be more front-loaded by summarizing key capabilities before diving into args, but it remains efficient without waste.

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

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

Given the tool's complexity (8 parameters, no annotations, no output schema), the description is largely complete. It covers purpose, parameters, and behavioral aspects like filtering and pagination. However, it lacks output details (e.g., format of returned metadata) and error scenarios, which are important for a list tool with many filters.

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

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

The schema description coverage is 0%, so the description must fully compensate. It provides comprehensive semantics for all 8 parameters, including purpose, default values, special values (e.g., 'me', 'nobody'), interactions (e.g., 'my_dashboards_only' overrides 'owner'), and examples (e.g., 'name=*security*'). This adds significant value beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('List') and resource ('dashboards in Splunk'), specifies the types (Simple XML and Dashboard Studio), and enumerates the returned metadata fields. It distinguishes itself from siblings like 'get_dashboard_definition' (which retrieves a specific dashboard's content) by focusing on listing metadata across multiple dashboards.

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

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

The description implies usage through the detailed parameter explanations (e.g., filtering by owner, app, type), but it does not explicitly state when to use this tool versus alternatives like 'list_saved_searches' or 'get_dashboard_definition'. No exclusions or prerequisites are mentioned, leaving usage context inferred rather than directly guided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.