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contextburn

contextburn

by arsentev-ai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Run efficiency

run_efficiency

Measure the share of paid tokens that became model output versus re-reading context in recent local Claude Code sessions, over a chosen look-back window, by tokens and cost.

Instructions

Share of paid tokens that became model output versus re-reading of context already sent, over the last N hours of local Claude Code sessions. Reported by tokens and cost-weighted.

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": "run_efficiency"
  }
}

Input Schema

NameRequiredDescriptionDefault
hoursNoLook-back window in hours.

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 useful behavior: the data source (local Claude Code sessions), the aggregation basis (reported by tokens and cost-weighted), and the look-back scope. It does not state that it is a read-only computation or what the return shape looks like, which leaves gaps for a tool with zero annotation coverage.

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?

Two dense sentences with no filler, and the metric definition leads before the scope qualifier. It is tightly written, though the second sentence is slightly compressed.

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?

There is no output schema and no annotations, so the description must carry everything. It conveys the metric and its weighting but does not describe the return format or what the caller receives, leaving a modest gap for a zero-annotation tool.

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?

Schema description coverage is 100%, so the hours parameter (range, default 24) is already fully documented. The description's 'over the last N hours' adds no syntax or format detail beyond the schema, so the baseline of 3 is appropriate.

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?

The description defines the specific metric being reported: the share of paid tokens that became model output versus re-reading context, scoped to local Claude Code sessions. It is more than a restatement of the name 'run_efficiency'. However, it never distinguishes itself from its sibling spend_breakdown, so an agent gets a clear purpose but no sibling routing.

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?

There is no explicit when-to-use guidance, no conditions, and no mention of the sibling spend_breakdown. The agent is left to infer that this is a metric-style report versus a cost breakdown, purely from the description text.

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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