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CityEngine Cloud MCP Server

CityEngine Cloud MCP Server

by cgajs
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_quota

Checks the current API key's plan, generate/NL2CGA quota usage, and rate-limit parameters so clients can monitor remaining capacity and limits.

Instructions

查询当前 API key 的套餐、额度用量（generate/NL2CGA）与限流参数。

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_quota"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It does disclose that this is a read-style lookup scoped to the current API key and enumerates the three categories returned (plan, usage for generate/NL2CGA, rate limits), which is genuinely useful. However, it says nothing about whether the call itself consumes quota, whether usage is windowed over time, or what happens with an invalid key.

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?

A single front-loaded sentence with no filler; the scope (current API key) comes first and the returned data categories follow. Every element earns its place.

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 zero-parameter, no-output-schema tool, the description covers what matters: the identity scope and the three data categories returned. It falls short of 5 only because it omits the time window of the usage figures and any failure/edge-case behavior, which an agent might need to interpret results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters and the schema is an empty object, so there is nothing for the description to disambiguate. This is the baseline 4 case for a parameterless tool.

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 uses a specific verb (查询/query) with a clearly scoped resource: the current API key's plan, quota usage for generate/NL2CGA, and rate-limit parameters. An agent knows exactly what data comes back. It does not explicitly contrast itself with siblings like generate_cga or parse_cga, keeping it below a 5.

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 rather than stated: the phrase '当前 API key' signals that no arguments are needed and the call is scoped to the caller's own credentials. There is no explicit 'use this when…' clause and no mention of alternatives, so guidance is only inferable.

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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