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 key behaviors: the tool performs a read operation ('Get documents'), specifies output format ('documents array and count'), and discloses security constraints ('access and results are constrained by the authenticated user's permissions'). It does not mention rate limits, pagination, or error handling, but covers core operational and security aspects well.

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 and front-loaded with the core purpose, followed by clear sections for Args and Outputs/Security. Every sentence adds value: the first states purpose and use cases, the Args section explains parameters, and the final sentences cover outputs and security. There is no wasted text.

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 moderate complexity (3 parameters, no output schema, no annotations), the description is largely complete. It covers purpose, parameters, outputs, and security constraints. However, it lacks details on error cases, pagination for large result sets, or example outputs, which could enhance completeness for a read operation with filtering.

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?

Schema description coverage is 0%, so the description must fully compensate. It provides detailed semantic explanations for all three parameters: 'collection' as the collection name, 'app' as optional with default context, and 'query' as an optional MongoDB-style filter object with an example. This adds significant meaning beyond the bare schema, fully documenting parameter purposes and usage.

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 with specific verbs ('Get documents') and resources ('from a KV Store collection'), and distinguishes it from siblings by specifying 'MongoDB-style query filtering' and use cases ('fetch lookup/configuration data or narrow results by field values'). This is precise and differentiates it from other list/read tools in the sibling set.

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 context on when to use this tool ('fetch lookup/configuration data or narrow results by field values'), but does not explicitly state when not to use it or name specific alternatives among the many sibling tools (e.g., list_kvstore_collections, get_metadata). The guidance is helpful but lacks explicit exclusions or named alternatives.

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