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Smart Quota Orchestrator

Smart Quota Orchestrator

by ElNeMeR70
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Python
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sqo_memo_cache

Caches and retrieves expensive subsystem analyses using SHA-256 content hashes, auto-invalidating answers when bound source files change to prevent stale results and redundant token spend.

Instructions

SHA-256 Content-Hash Invalidated Semantic Memoization Cache: stores/retrieves expensive subsystem analyses in 2ms (100% token savings) and auto-invalidates when any bound source file changes on disk.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "action": "<action>",
      "workspace": "<workspace>"
    },
    "name": "sqo_memo_cache"
  }
}

Input Schema

NameRequiredDescriptionDefault
filesNoSource files whose SHA-256 hashes validate this cached answer.
actionYesMemoization cache operation.
answerNoSynthesized answer to cache when action='put'.
questionNoAnalytical question or lookup key.
workspaceYesWorkspace root directory.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv13.0.0

TDQS

B3.1/5.0
Behavior3/5

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

With no annotations, the description carries the full burden and does disclose genuine behavioral traits: automatic invalidation tied to bound file hashes, ~2ms retrieval, and token savings. It omits what happens on a miss, whether entries are workspace-scoped or persisted to disk, and what 'stats' reports, leaving meaningful gaps for a stateful tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

It is a single front-loaded sentence, but density is achieved through jargon stacking ('SHA-256 Content-Hash Invalidated Semantic Memoization Cache') and a promotional parenthetical '(100% token savings)' that spends words without adding actionable meaning.

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

Completeness3/5

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

The tool has five parameters across three distinct actions, which warrants per-action explanation that the description does not provide. Safety is not a concern for a cache, and the schema covers parameter shapes, but the get/put/stats workflow and cache-key semantics are left to inference.

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 description coverage is 100%, so the baseline is 3. The description adds the linkage between 'files' hashes and cache validity, but never explains that 'question' is the lookup key or that 'answer'/'files' are only meaningful for action='put'.

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 verb pair (stores/retrieves) and resource (memoization cache for expensive subsystem analyses), plus its defining mechanism (SHA-256 content-hash invalidation). It is distinguishable from most siblings, though it never positions itself against likely-adjacent tools such as sqo_cache_audit or sqo_memory_vault.

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

Usage Guidelines2/5

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

Usage is only implied by 'expensive subsystem analyses' – there is no explicit when-to-use, when-not-to-use, or alternative named. The three actions (get/put/stats) are never routed: an agent gets no guidance on which action to call first, or when 'stats' is appropriate.

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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