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set_agent_budget

Define monthly inference budget for an agent. Set hard cap to block requests when exceeded, soft cap for warnings, and choose enforcement action.

Instructions

Set or update the monthly inference budget for an agent. When the agent exceeds the hard cap, the LLM proxy blocks further requests (429).

Args: agent_id: The agent identifier. monthly_limit_usd: Monthly spend limit in USD. soft_cap_pct: Percentage at which a warning is emitted (default 80). hard_cap_pct: Percentage at which requests are blocked (default 100). action: Enforcement action at hard cap — "reject" (default) or "warn".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionNoreject
agent_idYes
hard_cap_pctNo
soft_cap_pctNo
monthly_limit_usdYes
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses the behavioral consequence of exceeding the hard cap (blocking requests with 429). However, it does not mention whether the budget change is immediate, reversible, or any required permissions. Transparency is adequate but not thorough.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise: two introductory sentences and a bullet-like parameter list. Every sentence adds necessary information, and there is no redundancy or fluff. The structure is clean and easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description does not explain the return value (e.g., success confirmation) or error cases. However, for a mutation tool that sets a budget, the core functionality is well-covered. Minor gaps in output and error behavior prevent a perfect score.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description lists all five parameters with clear meanings: agent_id (identifier), monthly_limit_usd (spend limit), soft_cap_pct (warning percentage), hard_cap_pct (block percentage), action (enforcement options). This adds significant value over the bare schema, though it could include expected formats or valid ranges.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Set or update the monthly inference budget for an agent.' It also explains the effect when the hard cap is exceeded, which adds specificity. The verb 'set or update' combined with the resource 'monthly inference budget' is precise and distinguishes it from the sibling tool 'get_agent_budget'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not explicitly state when to use this tool versus alternatives, nor does it provide prerequisites or exclusions. The usage context is implied through the description of the tool's function, but no guidance is given for scenarios like reading the budget (get_agent_budget) or handling multiple agents.

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

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