hcl-mcp

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spectral_sky_from_epw

Reconstructs a 1nm spectral sky from an EPW hour, returning a canonical dict. Optionally computes ground-glow for tilted receivers using custom albedo, engine, and atmospheric options.

Instructions

Physical spectral sky (1nm direct + sky-diffuse) reconstructed from an EPW hour.

Returns the canonical record as a dict. If surface_tilt_deg is given, also returns the spectral ground-glow term for that receiver tilt. spectral_albedo (len 401, 380-780nm @1nm) overrides the default gray albedo — pass import_geometry(...)["ground_albedo"] to couple scene ground reflectance to the sky ground-glow term. engine: spectrl2 (default) | smarts (requires SMARTSPATH).

Opt-in refinements (defaults reproduce prior output): ozone_mode='climatology' computes ozone from the EPW LOCATION lat/lon + date (van Heuklon 1979); aerosol_g overrides the 0.65 asymmetry; aod_anchor_nm converts a declared-wavelength AOD (e.g. 550) to tau500 via the Angstrom exponent. smarts_aerosol (engine="smarts" only): None/"rural" (default) or "user" to make the Ångström exponent + asymmetry factor settable via the SMARTS Card 8 USER aerosol model. cloud_variant: which SEDES1 cloud CCM to apply to the sky-diffuse spectrum — 'reanchored' (default, byte-identical to omitting this arg) | 'verbatim' (faithful Fig.11 CCM) | 'seed' (legacy eyeballed 9-anchor seed) | 'local' (your own locally-fitted CCM, requires ccm_coeffs). ccm_coeffs: path to a fitted CCM CSV (from sedes1_fit) — required when cloud_variant='local'.

Input Schema

TableJSON Schema

Name	Required	Default
`day`	Yes
`epw`	Yes
`hour`	Yes
`month`	Yes
`engine`	No	spectrl2
`aerosol_g`	No
`overrides`	No
`ccm_coeffs`	No
`ozone_mode`	No
`aod_anchor_nm`	No
`cloud_variant`	No
`smarts_aerosol`	No
`spectral_albedo`	No
`surface_tilt_deg`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 9, 2026

TDQS

A4.7/5.0

Behavior5/5

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

No annotations exist, so the description carries the full burden. It discloses the return type, the conditional ground-glow term, default behaviors (defaults reproduce prior output), engine-specific requirements (SMARTS needs SMARTSPATH), and details for each cloud_variant. This is comprehensive for a 14-parameter tool.

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 description is dense yet well-structured, front-loading the main purpose and then grouping refinements logically. Every sentence adds value; the length is justified by the tool's complexity. No filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The output schema exists (not shown), so return values need no further detail. The description explains coupling with import_geometry, engine requirements, and all major parameters. For a 14-parameter tool with no annotations, this is exceptionally complete.

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?

With 0% schema coverage, the description must explain parameters, and it explains most (spectral_albedo length/range, ozone_mode, aerosol_g, aod_anchor_nm, smarts_aerosol, cloud_variant, ccm_coeffs). However, 'overrides' is left entirely unexplained, and surface_tilt_deg is only described behaviorally without syntax details. Still, the coverage is strong.

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 opens with a clear verb ('reconstructed') and resource ('physical spectral sky from an EPW hour'), and specifies the output ('canonical record as a dict'). It distinguishes itself from siblings like spectral_sky_native and reconstruct_spectrum by describing its inputs and the physics model used.

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?

Provides detailed guidance on when to use each optional parameter (e.g., ozone_mode for climatology, cloud_variant variants, engine selection, coupling with import_geometry). It does not explicitly compare against alternative sky tools (e.g., spectral_sky_native) or state when not to use this tool, but the parameter-level guidance is substantial and actionable.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.