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Smart Quota Orchestrator

Smart Quota Orchestrator

by ElNeMeR70
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

sqo_quota_pools

Analyze live model quota JSON to separate shared and independent reserve pools, track reset countdowns, and calculate safe percent-per-hour burn rates for billing-aware usage planning.

Instructions

Analyze live model quota JSON into shared vs. independent reserve pools, reset countdowns, and safe %/hour burn rates.

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": {
      "models_json": "<models_json>"
    },
    "name": "sqo_quota_pools"
  }
}

Input Schema

NameRequiredDescriptionDefault
models_jsonYesRaw JSON string returned by swarm(action='models', all=true).

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. 'Analyze' implies a pure read/compute operation with no mutation, which is the key safety signal, but the description says nothing about failure behavior on malformed or stale JSON, whether the numbers are estimates, or what 'safe' burn rate is measured against.

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?

One dense sentence with no filler, and the core operation is front-loaded before the list of derived outputs. The triple-noun output list is compact but borders on jargon that could have been spelled out slightly.

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?

With no output schema, the description does enumerate the return content (pool split, reset countdowns, %/hour burn rates), which is what an agent needs to decide the call is worth making. Gaps remain around result shape and the threshold defining a 'safe' rate, but for a single-parameter analysis tool it is close to sufficient.

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 the single parameter is already documented as the raw JSON from swarm(action='models', all=true). The description only reinforces that this is live quota JSON, adding no format, size, or freshness guidance beyond the schema, so baseline 3 applies.

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?

States a specific verb ('Analyze') and a specific resource ('live model quota JSON') plus the concrete outputs it derives: shared vs. independent reserve pools, reset countdowns, and burn rates. That is far more specific than a restatement of the name, though it never names a sibling tool or a boundary against other analysis tools.

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 only implied: 'live model quota JSON' plus the schema's note that input comes from swarm(action='models', all=true) tells an agent where to get the input, which effectively defines when to call it. There is no explicit when-not-to-use condition and no alternative named for related quota/budget questions.

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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