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Smart Quota Orchestrator

Smart Quota Orchestrator

by ElNeMeR70
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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sqo_context_pack

Compile a workspace or directory into a strict token budget, keeping focus files in full source and reducing supporting files to AST skeletons for compact context.

Instructions

Compile a multi-file directory or workspace into a strict token budget using full code for focus files and AST skeletons for supporting files.

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": {
      "workspace": "<workspace>"
    },
    "name": "sqo_context_pack"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathsNoOptional subdirectories/files to scope.
workspaceYesWorkspace root path.
max_tokensNoStrict token ceiling (default: 3500).
focus_filesNoHigh-priority files kept in full source.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv13.0.0

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It usefully discloses output composition, with full source for focus files and AST skeletons for supporting files, and states that the token budget is strict, but it does not clarify read-only versus mutation behavior, what happens when the budget is exceeded, or what the returned context pack looks like.

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 definition is a single front-loaded sentence that states the verb, resource, constraints, and file-handling strategy without filler. The technical detail about AST skeletons earns its place because it explains the core output behavior.

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 core mechanism and strict token budget are covered, which is enough to understand the tool's basic function. However, with no annotations and no output schema, the one-sentence description leaves output structure, failure behavior, and selection guidance unstated.

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 schema already documents all four parameters, making 3 the baseline. The description adds meaning by explaining that focus_files are kept in full source while supporting files become AST skeletons, but workspace and paths semantics are left entirely to the schema.

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 gives a specific verb, 'Compile', a resource, 'multi-file directory or workspace', and the mechanism used: full code for focus files and AST skeletons for supporting files. It is clear what the tool does, but it does not name a sibling alternative to differentiate itself explicitly from tools like sqo_compress_prompt or sqo_dependency_slices.

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?

There is no explicit when-to-use or when-not-to-use guidance, and no alternative tool is named. The token-budget framing implies a context-packing use case, but selection guidance is left to inference.

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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