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codexwatch

codexwatch

by psychofict
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

quota_forensics

Breaks down what each percentage point of your Codex plan costs in API-equivalent dollars and tokens per window and day, and flags quota rises that local logs cannot explain.

Instructions

What each percentage point of the plan cost in API-equivalent dollars and tokens, per window and per day, plus rises in a window that the local logs cannot explain (usage from another machine, ChatGPT itself or cloud tasks, or a metering change).

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": "quota_forensics"
  }
}

Input Schema

NameRequiredDescriptionDefault
windowNoonly windows of this length; both when omitted

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior3/5

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

No annotations exist, so the description carries the behavioral burden, and it does disclose something meaningful: the tool attributes unexplained rises to external causes (another machine, ChatGPT itself, cloud tasks, or a metering change), which tells the agent the analysis is inferential and not authoritative. It still omits whether the tool is read-only, how expensive/latency-heavy it is, and what the response shape looks like.

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?

It is one front-loaded sentence with the primary metric stated first and the anomaly caveat second; the parenthetical list of external causes is long but each item adds real diagnostic information. Slightly dense for a single sentence, but no filler.

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?

With zero required parameters, no output schema, and full schema coverage, the description covers what the tool returns and the limits of that attribution. The main remaining gap is operational context (cost, latency, or that this is a read-only analysis) which matters for an analytics tool an agent may call repeatedly.

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 coverage is 100% and the single enum parameter is documented in the schema, so baseline 3 applies. The description's 'per window and per day' hints at the reporting granularity but adds no selectable semantics beyond what the enum already provides.

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 states a specific analytic output: cost per percentage point of plan in API-equivalent dollars and tokens, per window and per day, plus unexplained in-window rises. That is concrete and distinguishable in substance, but it never names or contrasts the siblings (usage_summary, daily, plan_limits) that overlap most, leaving the agent to infer the boundary.

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 clause about 'rises in a window that the local logs cannot explain' implies the diagnostic use case where this tool beats the plain summaries. However, there is no explicit when-to-use / when-not-to-use statement and no routing to alternatives, so guidance remains implied rather than stated.

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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