analyze

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

Beyond the annotations (readOnlyHint: true), the description adds that the tool is local, makes no provider calls, and writes nothing. It also reveals the return format (text-wrapped JSON envelope) and the slash command rendering. This provides comprehensive behavioral insight.

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 a single paragraph of four sentences, front-loaded with the main action. It is concise but uses technical terms that may be dense. Every sentence adds value, but a more structured format (e.g., bullet points) could improve readability.

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 (two lenses, advisory suggestions) and the absence of an output schema, the description adequately explains the return format, prerequisites, and constraints. It covers all essential aspects for an agent to invoke the tool correctly.

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 coverage is 100%, so the schema already documents both parameters. The description provides a brief paraphrase ('how many recent session records', 'tail size of debug log') but does not add substantial new meaning beyond the schema descriptions. Baseline 3 is appropriate.

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 analyzes recent runs from debug log and session store to return advisory tuning suggestions. It specifies the two lenses (timing and agreement) and their independence, which distinguishes it from generative tools like ask-all. The purpose is unambiguous and actionable.

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 mentions a prerequisite (debug.enabled for the timing lens) and implies usage context for making tuning decisions. However, it does not explicitly state when to avoid this tool or provide alternatives among the sibling tools. The context is clear but lacks exclusions.

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