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Smart Quota Orchestrator

Smart Quota Orchestrator

by ElNeMeR70
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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sqo_smart_outline

Extract compact AST and symbol signatures with line ranges from large source files to cut token use, or pull one specific symbol body by name.

Instructions

Extract compact AST/symbol signatures and Lstart-Lend line ranges from a large source file (saves 85-96% tokens vs reading full file), or extract one specific symbol body.

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_smart_outline"
  }
}

Input Schema

NameRequiredDescriptionDefault
symbolNoOptional class/function/method name to extract with line numbers.
file_pathYesAbsolute path to the file to outline.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv13.0.0

TDQS

A3.5/5.0
Behavior3/5

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

No annotations are supplied, so the description carries the full burden. It does disclose useful behavioral traits: output is compact signatures with line ranges, and it claims 85-96% token savings versus full-file reads. It says nothing about failure modes (missing file, binary/non-source input), language coverage, truncation on very large files, or multiple definitions of the same symbol name.

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?

A single front-loaded sentence with the primary action first and the token-saving rationale parenthesized. Appropriate length for the tool's simplicity, with no filler.

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

Completeness4/5

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

For a two-parameter read tool with no output schema, the description covers what is produced (signatures and line ranges) and why it matters (token savings). The remaining gaps are edge-case behavior rather than information needed to make a correct invocation.

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% and both parameters are already documented, so the baseline of 3 applies. The description does add a subtle distinction by calling the symbol mode a body extraction (versus signatures in outline mode), but otherwise it restates what the schema already says.

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?

Specific verb (extract) and resource (AST/symbol signatures plus Lstart-Lend line ranges) from a source file, with the dual mode spelled out: whole-file outline or a single symbol body. It implicitly distinguishes itself from simply reading the file by citing token savings, but never names a sibling tool for that alternative.

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?

Implies the main selection condition: use this instead of reading a full file when you need signatures rather than content, and use the symbol parameter when you want one definition. There is no explicit when-not-to-use guidance and no named alternative tool, so guidance is inferred rather than stated.

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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