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 adds valuable context beyond basic functionality: it specifies the output format ('array of collections with name, fields, accelerated_fields, replicated; and total count'), security constraints ('results are constrained by the authenticated user's permissions'), and the optional filtering parameter. This covers key behavioral aspects like return structure and access control, though it doesn't mention rate limits or pagination.

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 efficiently structured: it starts with the core purpose, then usage guidelines, output details, security note, and parameter explanation—all in four concise sentences. Each sentence adds clear value without redundancy, making it easy to parse and front-loaded with essential information.

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 no annotations and no output schema, the description provides strong contextual completeness for a read-only listing tool. It covers purpose, usage, output format, security, and parameters. The main gap is the lack of explicit mention of pagination or limits, which is common for list operations, but it's still highly informative for the tool's complexity.

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 input schema has 0% description coverage (no parameter descriptions), but the description compensates well. It explains the single parameter 'app' as 'Optional app name to filter collections,' clarifying its purpose and optional nature. Since there's only one parameter and the description fully documents it, this exceeds the baseline of 3 for high schema coverage or 4 for zero parameters.

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 tool's purpose: 'List KV Store collections with basic schema details.' It specifies the verb ('List') and resource ('KV Store collections'), and distinguishes it from siblings like 'get_kvstore_data' (which retrieves data) and 'create_kvstore_collection' (which creates collections). However, it doesn't explicitly differentiate from other list tools (e.g., 'list_lookup_definitions'), keeping it at 4 instead of 5.

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 provides clear usage context: 'Use this to discover available KV stores for lookups, configuration, or caching, optionally filtering by app.' This indicates when to use it (for discovery purposes) and mentions the optional filtering capability. It doesn't explicitly state when not to use it or name alternatives (e.g., vs. 'get_kvstore_data'), so it scores 4 rather than 5.

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