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contextburn

contextburn

by arsentev-ai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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Spend breakdown

spend_breakdown

Break down token spend over the last N hours to reveal run efficiency, session costs, and what inflated the context.

Instructions

Human-readable breakdown of token spend over the last N hours: run efficiency, sessions, and what specifically inflated the context.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "spend_breakdown"
  }
}

Input Schema

NameRequiredDescriptionDefault
hoursNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.1

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, and it does disclose the output is human-readable and time-windowed, implying a read-only reporting operation. It stops short of stating permissions, cost, or whether the window is inclusive — gaps that are modest but real for an annotation-free tool.

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

Conciseness4/5

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

One efficient sentence with the output shape front-loaded after the resource. No filler, though the colon-list format lists features rather than prioritizing what the agent needs most.

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

Completeness3/5

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

For a single-parameter, annotation-free read tool with no output schema, the description covers the purpose, window, and report contents adequately. It leaves open the return structure for a self-described 'human-readable' report and gives no hint about sibling selection, so it is viable but not complete.

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

Parameters3/5

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

Only one parameter at 0% schema description coverage, but the description explains it semantically ('over the last N hours'), which is the single most useful thing to know. It omits the default (12), bounds (0.1–720), and what happens when hours is left unset, so it adds meaning without fully compensating.

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

Purpose4/5

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

States a specific resource (token spend) and scope (last N hours) plus the report's contents (run efficiency, sessions, context inflation). It is clear on its own, but it doesn't distinguish itself from the sibling run_efficiency — in fact it names 'run efficiency' as a component, which could make an agent wonder whether to call this or that sibling.

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

Usage Guidelines2/5

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

No explicit when-to-use guidance and no routing between this tool and run_efficiency. Usage is only implied by the reporting-oriented framing; nothing tells the agent when this is the right call versus the sibling.

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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