SigRank MCP server

diagnose_cascade

Read-onlyIdempotent

Analyzes four token pillars to identify efficiency leaks, ranked by severity with recommendations. Checks cache leverage, velocity, SNR, input bloat, and compounding before testing fixes.

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

Name	Required	Description	Default
`text`	Yes	Token pillars — ccusage JSON or "input output cacheCreate cacheRead" (same format as rank_paste).

Output Schema

TableJSON Schema

Name	Required	Description
`cascade`	No
`pillars`	No	The 4 raw token pillars
`summary`	No	One-line summary of the operator's cascade health
`diagnosis`	No	Ranked list of efficiency leaks found, worst first

Schema Changelog

Changes observed during successful MCP inspections.

Changed9 schema fields changedv0.19.6Aug 11, 2026

changedOutput schema / properties / cascade / properties / class / description

Previous value: -"Operator class tier"New value: +"Operator class tier (dev10x taxonomy)"

changedOutput schema / properties / cascade / properties / class / enum

Previous value: -[
-  "Burner",
-  "Builder",
-  "10xer"
-]New value: +[
+  "ARCH+ I",
+  "ARCH+ II",
+  "ARCH+ III",
+  "ARCH I",
+  "ARCH II",
+  "ARCH III",
+  "POWER I",
+  "POWER II",
+  "POWER III",
+  "BASE I",
+  "BASE II",
+  "BASE III",
+  "SEEKER I",
+  "SEEKER II",
+  "SEEKER III",
+  "REFINER I",
+  "REFINER II",
+  "REFINER III",
+  "BEARER I",
+  "BEARER II",
+  "BEARER III",
+  "IGNITER I",
+  "IGNITER II",
+  "IGNITER III",
+  "UNCLASSED"
+]

addedOutput schema / properties / cascade / properties / dev10x

Added value: +{
+  "description": "10xDEV score (log10 of the cascade product)",
+  "type": "number"
+}

addedOutput schema / properties / cascade / properties / mode

Added value: +{
+  "description": "Detected working mode (BUILD/EDIT/DEBUG/MAINTAIN/IDLE) + confidence",
+  "type": "object"
+}

addedOutput schema / properties / cascade / properties / pillars

Added value: +{
+  "description": "The four raw token pillars the cascade was computed from",
+  "type": "object"
+}

removedOutput schema / properties / cascade / properties / tenx_dev

Removed value: -{
-  "description": "10xDEV score",
-  "type": "number"
-}

addedOutput schema / properties / cascade / properties / yield

Added value: +{
+  "description": "Υ Yield — the headline efficiency metric (Cache Reads × Output / Input²)",
+  "type": "number"
+}

removedOutput schema / properties / cascade / properties / yield_

Removed value: -{
-  "description": "Υ Yield — the headline efficiency metric (Cache Reads × Output / Input²)",
-  "type": "number"
-}

changedOutput schema / properties / cascade / required

Previous value: -[
-  "yield_",
-  "class"
-]New value: +[
+  "yield",
+  "class"
+]

Addedv0.19.3Jul 26, 2026
Removedv0.19.2Jul 23, 2026
Addedv0.16.0Jul 7, 2026

TDQS

A4.9/5.0

Behavior5/5

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

Even with readOnlyHint/idempotentHint annotations, the description adds meaningful behavioral context: 'Pure local math — no network, no submission' and describes output structure (ranked leaks, severity, findings, recommendations, estimated impact).

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 long but every sentence serves a purpose: purpose, checks performed, output details, and safety caveat. It is front-loaded and well organized for a complex analysis tool.

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 output schema exists, the description need not detail return values, but it still covers what checks are performed, output type (ranked list with severity/findings/recommendations), and the local/no-submission behavior, making it complete.

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 already documents the 'text' parameter at 100% coverage, so baseline is 3. The description adds value by specifying 'JSON or 4 whitespace numbers' and noting it accepts the same format as rank_paste, clarifying the expected input forms.

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 opens with a specific verb ('Analyzes your token cascade and diagnoses where you're leaking efficiency') and names the resource. It distinguishes from siblings by positioning it as the pre-simulation diagnostic and referencing rank_paste's input format.

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

Usage Guidelines5/5

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

Explicit usage guidance: 'Use this BEFORE simulate_change to understand what's wrong, then use simulate_change to test fixes.' This clearly tells the agent when to invoke this tool relative to alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.