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Weather MCP Server by Meteosource

get_solar_production

Production of photovoltaic systems, past and forecast, for up to 20 sites.

Each site is lat + lon plus the installation: module_kw (installed kWp;
use 1 to get specific yield per kWp), inverter_kw (defaults to module_kw),
tilt (degrees from horizontal, default 30) and orientation (azimuth the
modules face, clockwise from north: 180 = south, the default). Dates are
YYYY-MM-DD, inclusive, from up to 20 years back to 4 days ahead, so one
call can cover last week's actual weather and the coming days. The
renewables plan includes the last 3 years; older days need the paid
historical add-on, otherwise the API refuses them ("historical data this
old"), reported per site.
Granularity: 'hourly' (power at each full hour, max 14 days per call),
'daily' or 'monthly' (energy, max 366 days per call — call repeatedly for
multi-year yield; past days are cached so repeats are cheap).
Columns: production_kw = output of the system at that hour (an
instantaneous value); production_kwh = energy of the day or month, summed
from those hourly values; production_max_kw = its peak.
`variables` adds the weather behind the production: irradiance (ghi, dni,
dhi in W/m2 hourly; ghi/dni/dhi_sum_wh_m2 daily and monthly), temperature,
cloud_cover, humidity, dew_point, pressure, snowfall (flag per hour;
snowfall_hours daily and monthly).
Each site+day costs one API request on first fetch; a single call is
capped at 400 site-days — split longer ranges into multiple calls.
Needs a key on the renewables tier.

Output: `columns` once at the top level; each site has `rows` as arrays
in column order.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sitesYes
unitsNometric
date_toYes
timezoneNoauto
date_fromYes
variablesNo
granularityNodaily

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations to lean on, the description carries the full behavioral burden and does so: it discloses the renewables-tier key requirement, the 400 site-day cap, one-request-per-site-day costing, caching of past days, per-site refusal of out-of-plan history, and the exact error wording. This is unusually thorough.

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?

Long but densely packed with no filler; the first sentence front-loads purpose and scale before drilling into site fields, date rules, granularity, columns and cost. It could be split for scannability, but almost every sentence carries actionable information.

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?

No output schema exists, yet the description defines the return shape (top-level `columns`, per-site `rows` arrays in column order) and the meaning of each production column. Combined with the auth, cost and error behavior, an agent has everything needed to call this correctly.

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?

Top-level schema coverage is reported as 0%, so the description must compensate, and it largely does: it explains module_kw=1 for specific yield, inverter_kw defaulting to module_kw, tilt/orientation defaults and azimuth convention, date format and bounds, granularity options and their limits, and the variables list. However, `units` and `timezone` are never explained anywhere.

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?

States a specific verb and resource ('Production of photovoltaic systems, past and forecast') plus scope ('up to 20 sites'), which cleanly separates it from the sibling get_wind_production. An agent can tell exactly what this returns without opening the schema.

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?

Gives concrete selection guidance for the granularity parameter (hourly capped at 14 days, daily/monthly at 366 days) and date window (20 years back to 4 days ahead), and warns that older days need a paid add-on. It stops short of explicitly routing to sibling tools, but the usage context is clear.

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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