llmkit_local_forecast

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, covering safety. The description adds context about projection and comparison, but does not disclose additional behavioral traits like data freshness, assumptions, or update frequency. It adds some value beyond annotations but is not rich.

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 consists of two short sentences with no extraneous words. It is front-loaded with the core purpose and effectively communicates the tool's function in a concise manner.

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 no parameters, a present output schema, and annotations covering safety, the description is largely complete. It explains the purpose and output comparison, though it does not elaborate on what 'Max subscription' means or how projections are calculated. Still, it is sufficient for a simple 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?

There are zero parameters, and schema description coverage is 100% (trivially). Per guidelines, the baseline is 3 when coverage is high. The description does not need to add parameter information, so a score of 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 it provides a 'monthly cost projection based on local AI tool usage' and 'compares to Max subscription.' This is a specific verb-resource pair, and it distinguishes itself from siblings like llmkit_budget_status (budget status) and llmkit_cost_query (raw cost queries).

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 gives context by mentioning comparison to a 'Max subscription,' but does not explicitly state when to use this tool versus alternatives like llmkit_cost_query or llmkit_budget_status. Usage guidance is implied but not made explicit for the AI agent.

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