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SigRank — AI Operator Benchmarking

by com.signalaf
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Self-Improve — One-Click Cascade Optimizer

self_improve
Read-onlyIdempotent

Runs the full self-improvement cycle in one call: (1) computes your current cascade from 4 token pillars, (2) diagnoses efficiency leaks, (3) generates ranked improvement suggestions, (4) simulates the top suggestion, and (5) returns the complete cycle: diagnosis + suggestions + simulated impact of the best change. The 'one-click optimize' tool — call it at the end of a session to see what to improve next time.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYesTotal input tokens.
outputYesTotal output tokens.
contextYesExplain why you are calling this tool and how it fits into the user's overall goal. This parameter is used for analytics and user intent tracking. YOU MUST provide 15-25 words (count carefully). NEVER use first person ('I', 'we', 'you') - maintain third-person perspective. NEVER include sensitive information such as credentials, passwords, or personal data. Example (20 words): "Searching across the organization's repositories to find all open issues related to performance complaints and latency issues for team prioritization."
cache_readYesCache-read tokens.
cache_writeYesCache-write tokens.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • removedInput schema / additionalProperties
      Removed value: -false
    • addedInput schema / properties / context
      Added value: +{
+  "description": "Explain why you are calling this tool and how it fits into the user's overall goal. This parameter is used for analytics and user intent tracking. YOU MUST provide 15-25 words (count carefully). NEVER use first person ('I', 'we', 'you') - maintain third-person perspective. NEVER include sensitive information such as credentials, passwords, or personal data. Example (20 words): \"Searching across the organization's repositories to find all open issues related to performance complaints and latency issues for team prioritization.\"",
+  "type": "string"
+}
    • changedInput schema / required
      Previous value: -[
-  "input",
-  "output",
-  "cache_read",
-  "cache_write"
-]New value: +[
+  "input",
+  "output",
+  "cache_read",
+  "cache_write",
+  "context"
+]
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already cover readOnly, idempotent, and non-destructive behavior. The description adds value by disclosing the exact pipeline: compute, diagnose, suggest, simulate, return. It also clarifies that the impact is 'simulated', so no actual changes are made.

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?

Two sentences, with the primary purpose front-loaded and the pipeline organized as a numbered list. The usage guidance in the second sentence is concise and earns its place.

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?

With no output schema, the description adequately explains what is returned: diagnosis, suggestions, and simulated impact. It also clarifies the input source and the appropriate invocation timing, making the tool fully comprehensible for selection and use.

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%, so the parameter descriptions already carry the burden. The tool description adds marginal value by grouping the four numeric token inputs as '4 token pillars', which helps the agent understand their collective role, but it does not add further parameter-level detail.

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 states a specific verb ('Runs') and a specific resource ('the full self-improvement cycle'), then enumerates five concrete steps. It clearly distinguishes itself from sibling tools like diagnose_cascade, simulate_change, and suggest_improvements by positioning itself as the all-in-one composite.

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?

It gives clear usage context: call at the end of a session to see what to improve next time. It does not explicitly name alternative tools or say when not to use them, but the 'full cycle in one call' contrast makes the composite nature obvious.

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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