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Smart Quota Orchestrator

Smart Quota Orchestrator

by ElNeMeR70
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Python
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sqo_gen_tests

Generates ready-to-run Red-Green TDD test harnesses from Python, TypeScript/JavaScript, or Dart source files, reducing manual test creation and output-token use.

Instructions

Zero-Token Deterministic Unit & Edge-Case Test Synthesizer (v11.0 Hypernova): parses Python, TS/JS, or Dart source files and generates ready-to-run Red-Green TDD test harnesses (saving ~3,000 Output Tokens per module!).

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": {
      "file_path": "<file_path>"
    },
    "name": "sqo_gen_tests"
  }
}

Input Schema

NameRequiredDescriptionDefault
file_pathYesTarget source file path.
output_pathNoOptional path to write generated test file.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv13.0.0

TDQS

B3/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden. It discloses that generation is deterministic and language-specific, but it does not explain whether output_path overwrites an existing file, what permissions are needed, or what happens on parse failure.

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?

The core purpose is front-loaded after branding and version fluff. The parenthetical version label and the token-savings claim do not help an agent decide how to invoke the tool.

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

Completeness2/5

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

For a file-writing generator with no annotations and no output schema, the description should clarify return values and side effects such as overwrite behavior and failure cases. It mentions ready-to-run tests but leaves those operational details missing.

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 schema already documents file_path and output_path. The description adds no parameter-specific meaning beyond the supported source languages, making the baseline 3 appropriate.

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 and resource: it parses Python, TS/JS, or Dart source files and generates ready-to-run TDD test harnesses. That is enough to distinguish it from sibling tools like sqo_code_search or sqo_dead_code, though it does not name an alternative explicitly.

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

Usage Guidelines3/5

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

Usage is implied by the supported languages and the TDD test-generation goal, but the description gives no explicit when-to-use vs when-not-to-use guidance and does not mention prerequisites or alternatives.

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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