llmkit_local_cache

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the tool is clearly safe and idempotent. The description adds context about prompt caching but does not disclose any additional behavioral traits beyond what annotations provide.

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 extremely concise: two sentences that immediately state the tool's function without extraneous information. Every sentence adds value, and there is no wasted space.

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?

Despite the short description, it is sufficient for a parameterless tool with annotations and an output schema. It clearly explains what the tool does ('cache savings analysis'), and the output schema presumably details the return format. No critical information is missing.

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?

The input schema has no parameters, so schema description coverage is effectively 100%. The description does not need to explain parameters. According to guidelines, 0 parameters yields a baseline score of 4.

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: 'Cache savings analysis across all detected AI coding tools. Shows how much prompt caching saved.' It uses specific verbs ('analysis', 'shows') and identifies the resource (cache savings), effectively distinguishing it from sibling tools like 'llmkit_cost_query' or 'llmkit_budget_status' which focus on other aspects.

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 implies usage for cache savings queries but does not explicitly provide when-to-use vs alternatives or list exclusion criteria. Given the tool's name and title, the context is clear, but there is no explicit guidance on when not to use it or how it compares to sibling tools.

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