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diagnose_cascade

Read-onlyIdempotent

Identify token cascade inefficiencies and get ranked findings with severity and recommendations to improve yield.

Instructions

Analyzes your token cascade and diagnoses where you're leaking efficiency. Takes your 4 pillars (input/output/cacheCreate/cacheRead) and produces a ranked list of efficiency leaks with severity (critical/warning/info), findings, and recommendations. Checks: cache leverage (are you rereading what you wrote?), velocity (are you generating enough output per input?), SNR (is your signal drowning in noise?), cache creation ratio (are you over-committing?), input bloat (is fresh input too high?), and 10xDEV (is the full cascade compounding?). Each finding includes an estimated Υ impact. Pure local math — no network, no submission. Use this BEFORE simulate_change to understand what's wrong, then use simulate_change to test fixes. Accepts the same input formats as rank_paste (JSON or 4 whitespace numbers).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesToken pillars — ccusage JSON or "input output cacheCreate cacheRead" (same format as rank_paste).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
cascadeNo
pillarsNoThe 4 raw token pillars
summaryNoOne-line summary of the operator's cascade health
diagnosisNoRanked list of efficiency leaks found, worst first
Behavior5/5

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

Beyond annotations (readOnlyHint, idempotentHint), description adds 'Pure local math — no network, no submission' and details all checks performed.

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?

Fairly long but well-structured, front-loading purpose, then listing checks, then usage guidance. No redundancy.

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?

Complete given one parameter and presence of output schema: describes input format, analysis features, and usage context (before simulate_change).

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?

Schema coverage is 100%, but description adds format details (ccusage JSON or whitespace numbers) and references rank_paste for format consistency.

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?

Clearly states it analyzes token cascade and diagnoses efficiency leaks. Distinguishes from siblings by explicitly referencing simulate_change and rank_paste.

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?

Explicitly states when to use: before simulate_change. Lists checks performed. Could improve by stating when not to use, but context is sufficient.

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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