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epwforge-mcp

by io.github.guzz-labs

chart_weather

Render an SVG chart from EPW data. Eight chart types: diurnal (~10 KB, monthly hourly profile), temp_carpet (heatmap of hour × day-of-year — ~30 KB preview / ~150 KB full), wind_rose (~12 KB, polar bars by direction × speed), monthly_boxplot (~6 KB, Q1/median/Q3 + whiskers per month), utci_carpet (~90 KB, outdoor heat-stress hour × day, colored by UTCI category — Bröde 2012, shaded Tmrt), economizer_carpet (~90 KB, air-side economizer free / integrated / locked-out hour × day under ASHRAE 90.1 high-limit), pv_tilt_azimuth (~60 KB, annual PV generation across full tilt × azimuth space, isotropic-sky POA at lat from EPW header — optimum orientation marked), solar_under_events (~12 KB, weekly GHI of the modified scenario vs the no-overlay reference; bands color event-affected weeks. Requires config — server runs the pipeline twice, with and without overlays), comparison (~10 KB, design-condition deltas across EPWs). Accepts url (single), urls (2+ for comparison), or config (synthesize on the fly). Config mode is anon-safe — runs pipeline, returns SVG only. No auth required. Token budget: SVGs are returned inline by default. Large outputs (>50 KB) auto-upload to Blob storage (when configured) and return a URL instead, keeping your context lean. Always check svg_size_kb in the response. Presentation: when handing the chart to the user, just link or embed it — don't narrate what's in it. Let the chart speak.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNoEPW URL (single-EPW charts: diurnal, temp_carpet, wind_rose, monthly_boxplot, utci_carpet, economizer_carpet, pv_tilt_azimuth)
urlsNoEPW URLs for chart_type=comparison (first = baseline)
configNoSynthesize an EPW and chart it. Same params as generate_weather_file. SVG only — no file delivery.
pv_tiltNopv_tilt_azimuth only. Optional — marks the user's planned tilt (deg, 0=horizontal) on the heatmap alongside the optimum.
econ_modeNoeconomizer_carpet only. 'drybulb' (default) limits OA by Tdb; 'enthalpy' limits by moist-air enthalpy — more honest in humid climates (captures latent load).
chart_typeNoDefault: diurnal
pv_azimuthNopv_tilt_azimuth only. Optional — marks the user's planned compass azimuth (deg, 0=N, 180=S) on the heatmap.
resolutionNotemp_carpet only. 'preview' (default) ~30 KB with 32 color buckets — visually identical at typical render sizes. 'full' ~150 KB with per-cell rgb() — use only when saving to file or when you specifically need exact color fidelity.
econ_high_limit_fNoeconomizer_carpet only. ASHRAE 90.1 high-limit shutoff. Defaults: 75°F (drybulb mode) or 28 BTU/lb (enthalpy mode).
econ_supply_air_fNoeconomizer_carpet only. Supply-air temperature setpoint (°F). Default 55.

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description discloses token budget, auto-upload behavior for large SVGs, presentation advice, and config mode anonymity. This provides solid behavioral context beyond the input schema.

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?

The description is well-structured with clear sections: chart types, input modes, token budget, presentation. It is longer than ideal but front-loaded with key information. Every sentence adds value, though some details (e.g., all chart types) could be slightly condensed.

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?

The description covers chart types, sizes, input modes, token budget, and presentation. It lacks output schema details, but that is acceptable. For a tool with 10 parameters and no annotations, it is fairly complete, though chart-type-specific nuances could be expanded.

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%, so baseline is 3. The description adds value for some parameters (e.g., resolution differences, event_duration guidance, intensity_auto auto-fill) but does not significantly enhance understanding of all 10 parameters 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 starts with 'Render an SVG chart from EPW data', which is a specific verb and resource. It lists all nine chart types with brief explanations, making the tool's purpose clear and distinguishing it from siblings like analyze_weather or generate_weather_file.

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 explains when to use each input mode (url, urls, config) and mentions chart-type-specific parameters. It does not explicitly state when not to use the tool, but context signals and the list of sibling tools imply alternatives exist.

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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TDQS

A4.2/5.0
Disambiguation4/5

Each tool has a distinct primary function: analysis, charting, interactive exploration, station search, file generation, and bundle preview. Some overlap exists between 'analyze_weather' and 'explore_design_conditions', but descriptions clarify single-site vs. batch and interactive vs. static usage.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern (e.g., analyze_weather, chart_weather, find_station). Verbs are action-oriented and nouns clearly indicate the target resource.

Tool Count5/5

With 6 tools, the server is well-scoped for EPW weather data tasks. Each tool serves a necessary function without superfluous additions, fitting the ideal 3-15 range.

Completeness4/5

Core workflows (find station, analyze stats, visualize, generate custom files, purchase) are covered. A minor gap is the lack of a direct raw file download tool, but the EPW URLs from 'find_station' effectively serve that purpose.

Resources