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ritn3d-mcp

ritn3d-mcp

by printplan3d
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

estimate_complexity

Estimates floor plan detection complexity by analyzing image resolution, brightness, and edge density. Returns a label and score for batch routing and setting correction expectations.

Instructions

Heuristic estimate of a floor plan's detection complexity. Reads basic image stats (resolution, brightness range, edge density) and returns a label (simple/moderate/complex) plus a numeric score. Useful for batch routing and for setting user expectations about how many corrections may be needed.

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": {
      "path": "<path>"
    },
    "name": "estimate_complexity"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathYesPath to the floor plan image.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries full burden. It specifies inputs (resolution, brightness range, edge density) and outputs (label, numeric score), and notes its heuristic nature. It lacks details on side effects or error handling but is sufficient for a read-only analysis.

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?

Two sentences: first defines purpose and output, second provides usage context. No wasted words.

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?

Given low tool complexity (1 parameter, no output schema), the description adequately covers purpose, inputs, outputs, and usage. It does not specify numeric score scale or error cases, but these are not critical for the tool's function.

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% for the single parameter 'path', which already describes it as a path to the floor plan image. The description adds no additional semantic value beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool estimates a floor plan's detection complexity using heuristic image analysis, returning a label and numeric score. It differentiates from siblings by focusing on complexity rather than render time or validation.

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

Usage Guidelines4/5

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

The description mentions usefulness for batch routing and setting user expectations, providing context for when to use. However, it does not explicitly state when not to use or compare to sibling tools.

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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