NitroWatch

get_burn_rate

Calculate how quickly your AI token budget is being consumed and project the runout date based on current usage.

Instructions

Project time-to-exhaustion on the team's 5M AI token budget

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "used": "<used>"
    },
    "name": "get_burn_rate"
  }
}

Input Schema

Name	Required	Description	Default
`used`	Yes
`budgetTotal`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 1, 2026

TDQS

B3.3/5.0

Behavior2/5

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

With no annotations, the description must disclose behavior itself. It does not state whether the tool is read-only, what assumptions underlie the projection (e.g., linear extrapolation), or what input 'used' represents. It also doesn't mention response format or error conditions, making behavior unpredictable.

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?

A single, clear sentence that puts the core purpose first. No wasted words. It conveys the essential idea efficiently, though it could benefit from additional detail elsewhere.

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

Completeness2/5

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

Without annotations or an output schema, the description needs to explain what the tool returns and any necessary context. It fails to state the output format, whether it returns a date, a number, or a report, and it lacks guidance on how to supply input. The description is too sparse for a complete understanding.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate. It mentions the 5M budget (budgetTotal) but does not explain the meaning of 'used' or how the projection is calculated from the parameters. The description adds minimal value in clarifying parameter semantics.

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?

Description uses a specific verb 'Project' and identifies the resource ('time-to-exhaustion on the team's 5M AI token budget'), clearly distinguishing this from sibling tools focused on server registration, approvals, and trust. It states the core function without ambiguity.

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 implies the tool is used to forecast when the token budget will run out, but it does not explicitly state when to use it versus alternatives, nor does it mention prerequisites like what 'used' represents or how to interpret the result. It offers only implied usage context.

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

Deploy Server