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 describes what the tool does (analyzes metadata, inspects execute method, generates documentation improvements) and provides details about the response format (dictionary with status and data fields). However, it doesn't mention potential limitations like processing time, error conditions, or prerequisites for successful execution.

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 appropriately sized and front-loaded with the core purpose and usage guidelines. The parameter explanations and response format details are organized clearly. While comprehensive, every sentence serves a purpose, though some redundancy exists between the initial description and the parameter explanations.

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 (analyzing and enhancing other tools) and the absence of both annotations and output schema, the description provides substantial context about what the tool does, when to use it, parameters, and response format. However, it could benefit from more details about the enhancement process or limitations of the analysis.

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?

With 0% schema description coverage, the description compensates well by explaining all three parameters: tool_name (with examples), generate_examples (purpose and default), and include_response_format (purpose and default). It adds meaningful context beyond the bare schema, though it could provide more detail on parameter constraints or validation rules.

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 ('analyzes', 'enhances', 'improves', 'generates') and resources ('MCP tools', 'descriptions', 'documentation'). It distinguishes itself from sibling tools by focusing on tool description enhancement rather than data retrieval or configuration management, making its purpose unambiguous and well-defined.

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 explicitly states when to use this tool: 'Use this tool when you need to improve or generate better documentation for a tool, such as adding examples or clarifying parameters.' It provides clear context for its application, distinguishing it from sibling tools that perform operations like data retrieval or configuration management.

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