get_llm_budget

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

With no annotations, the description fully covers behavior: it is a read-only operation (implicit by 'Get'), returns specific fields, mentions auto-reset at month boundaries, and states availability to all tiers. No contradictions.

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 two sentences, front-loads the purpose and response fields, and provides usage context efficiently. Every sentence adds value.

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 output schema, the description thoroughly explains the response fields and includes important context like the default budget, auto-reset behavior, and usage scenarios. It is complete for an AI agent to use 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?

The input schema has no parameters (100% coverage), so the description does not need to document parameters. It adds value by explaining the response structure and usage context 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 the tool retrieves the current monthly LLM feature budget, specifies the three axes it covers, and lists the response fields. It distinguishes from sibling tools like 'raise_llm_budget' and 'get_budget_gate' by explicitly stating its read-only nature and purpose for checking budget thresholds.

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 clear context for when the tool is used: by an AI agent deciding if the budget has hit 80% or if it should be raised. It notes availability across tiers. However, it does not explicitly mention when not to use it or compare with alternatives like 'raise_llm_budget' or 'get_budget_gate'.

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