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 the tool as an update operation with selective modification and preservation of unchanged settings, which implies mutation behavior. However, it lacks details on permissions needed, whether changes are reversible, rate limits, or error handling. The 'Response Format' section adds some context on output, but overall behavioral traits are under-specified for a mutation tool.

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 and response format. It is appropriately sized for a tool with 10 parameters, though the 'Args' section is lengthy. Every sentence adds value, but it could be more front-loaded by emphasizing key usage points earlier.

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 complexity (10 parameters, mutation tool) and no annotations or output schema, the description is partially complete. It covers parameters thoroughly and includes response format details, but lacks behavioral context like permissions, side effects, or error cases. For a mutation tool, this leaves gaps that could hinder agent understanding.

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 compensate fully. It provides detailed parameter semantics in the 'Args' section, explaining each of the 10 parameters with names, types, optionality, and examples (e.g., for earliest_time: '-24h@h', '-7d'). This adds significant meaning beyond the bare schema, effectively documenting all 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: 'Update an existing saved search's configuration including query, scheduling, and other properties.' It specifies the verb ('update') and resource ('saved search'), and mentions the scope of updates (query, scheduling, properties). However, it doesn't explicitly differentiate from sibling tools like 'create_saved_search' or 'delete_saved_search' beyond the update action, which is implied but not stated.

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 no explicit guidance on when to use this tool versus alternatives. It mentions 'selective modification' and 'preserving unchanged settings,' but does not indicate prerequisites, when not to use it, or refer to sibling tools like 'create_saved_search' or 'delete_saved_search' for context. Usage is implied through the action, but lacks clear directives.

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