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watch_tokenpull

Poll your local token logs at a configurable interval, detect new sessions by diffing against the last snapshot, and return the updated cascade when changes occur.

Instructions

Watch your local token logs and re-derive your cascade whenever new sessions are written — a live tune meter. Polls at a configurable interval (default 60s), diffs against the last snapshot, and returns the updated cascade when something changes. The push-to-board step is TODO(AUTH.WIRE) — currently returns the diff locally so you can see your score move in real time without submitting.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
platformNoplatform to watch (default: claude)
interval_sNopoll interval in seconds (default: 60, min: 10)
windowNowhich window to watch (default: 7d — most sensitive to recent activity)
codenameNoTODO(AUTH.WIRE): when set, will auto-submit on change once auth is live
Behavior4/5

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

With no annotations provided, the description fully covers behavioral traits: it polls at a configurable interval, diffs against a snapshot, returns the updated cascade locally, and notes the push-to-board step is TODO. It is transparent about its non-destructive nature and limitations.

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 concise with two sentences. The first sentence immediately states the primary purpose, followed by necessary detail on polling, diff, and limitations. No redundant or unnecessary information is present.

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

Completeness5/5

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

Given the tool has 4 parameters, no output schema, and no annotations, the description is complete. It explains the return value (diff/updated cascade), polling behavior, configurable parameters, and current limitations. It covers all aspects needed for an agent to use the tool correctly.

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?

All four parameters are fully described in the schema (100% coverage), so baseline is 3. The description adds meaningful context beyond the schema: default values for 'platform' (claude) and 'interval_s' (60, min 10), the sensitivity of 'window' (7d), and that 'codename' is for future auto-submit. This extra information warrants a score of 4.

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: watching local token logs and re-deriving the cascade when new sessions are written. It uses a specific verb ('watch') and resource ('local token logs'), and distinguishes itself from siblings like 'tokenpull_submit' by noting that the push-to-board step is not yet implemented, making it a live monitoring tool.

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

Usage Guidelines4/5

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

The description explains when to use the tool (for real-time monitoring of token logs) and provides details on polling behavior and configurable parameters. However, it does not explicitly mention when not to use it or name alternatives, though the context of sibling tools implies the distinction.

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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