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rhino-mcp

by rokibul-mist
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
Python
Hybrid

rhino_daylight_factor

Read-onlyIdempotent

Compute daylight factor for side-lit rooms using the simplified BRE method, evaluating early-stage architectural daylighting from window area, sky angle, and surface properties.

Instructions

BRE simplified daylight factor for a side-lit room.

    DF = (W * theta * tau * M) / (A_total * (1 - R^2)) (% as decimal).
    For early-stage architectural studies; for radiance-grade accuracy
    use ladybug-tools or DAYSIM.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
argsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already certify read-only and idempotent behavior. The description adds the exact formula, the output unit ('% as decimal'), and the side-lit-room constraint, which are useful beyond the annotations. It does not clutter with return-type details since an output schema exists.

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?

Three short lines: purpose, formula, and usage guidance. No filler; the most important information is front-loaded.

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 calculation tool with an output schema, read-only annotations, and a formula that ties parameters together, the description is nearly complete. It could be improved by naming sibling tools for comparison, but the required information for calling it correctly is present.

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

Parameters5/5

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

Schema description coverage is 0% per context, and the description compensates by presenting the formula with every variable (W, theta, tau, M, A_total, R) that maps to the six schema parameters. It clarifies the role of optional factors like transmittance, maintenance factor, and reflectance, and the schema supplies defaults and bounds.

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 clearly identifies the tool as a BRE simplified daylight factor calculation for side-lit rooms, including the governing formula. The method and application scope are evident, and the 'early-stage' qualifier distinguishes it from high-fidelity simulation tools, though it does not explicitly differentiate from sibling MCP analysis tools like rhino_direct_irradiance.

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?

It states when to use the tool ('early-stage architectural studies') and points to ladybug-tools/DAYSIM for radiance-grade accuracy. This gives an agent a clear heuristic for selecting it, though the alternatives are external rather than the sibling tools in this MCP.

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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