log_analyzer_ask

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

Annotations already indicate readOnlyHint=true and idempotentHint=true, and the description adds context by stating it provides 'intelligent, contextual answers' with 'supporting log entries and suggestions'. It also specifies parameter constraints like max_results range (10-200) and output format options, which are not in annotations.

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 brief introductory paragraph, a list of example questions, and an Args section. It is appropriately sized for the tool's complexity, though the example list could be slightly trimmed. No unnecessary sentences.

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 (AI-assisted log analysis), the description covers purpose, usage, parameter semantics, and output format. Since an output schema exists, the description does not need to detail return values. It is complete for an agent to correctly select and invoke the tool.

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?

Input schema has 0% description coverage, but the description's Args section provides meaningful explanations for all four parameters: file_path, question, max_results (with range and default), and response_format (with enumeration). This adds significant value beyond the 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 it answers questions about log files using AI-assisted analysis, with concrete examples that distinguish it from sibling tools like log_analyzer_search or log_analyzer_correlate. The verb 'ask' and resource 'log files' are specific and differentiated.

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 a list of example questions illustrating use cases, and implies it is for natural language queries rather than structured searches. However, it does not explicitly state when not to use or mention alternative tools for specific scenarios.

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