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DeepPVMapper

by gabrielkasmi
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_department_capacity_stats

Retrieve pre-computed total rooftop-PV capacity (kWp) and system counts for one or all French départements; use aggregate_detection_capacity for filtered subsets.

Instructions

Get installed rooftop-PV capacity (total kWp) and system counts for one or all French départements, from the DeepPVMapper/OpenPVMapper registry. This is a fixed, pre-computed department-wide aggregate with no other filters — use aggregate_detection_capacity instead if you need a filtered subset (e.g. only cross-validated detections). Data quality note: this is a detection dataset, not an exhaustive inventory (estimated recall ~0.6). A missing detection does not mean no PV installation exists there. kwp, surface, tilt and azimuth are model estimates, not surveyed values. In this server, quality_filter=true keeps only detections identified by 2 or more sources (see source_encoding in get_data_quality_reference). Call get_data_quality_reference for the full picture before assessing fitness for a specific use case.

Input Schema

NameRequiredDescriptionDefault
dptNoFrench département code, e.g. "33" for Gironde. Omit for all départements.
top_nNoIf set and dpt is omitted, return only the top N départements by installed capacity.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden, and it delivers unusually rich disclosure: fixed pre-computed aggregate with no other filters, ~0.6 recall, model-estimated kwp/surface/tilt/azimuth, and missing detections are not evidence of absence. It omits operational traits like auth/rate limits or response shape, keeping it just short of a 5.

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?

Front-loaded with the core purpose and the alternative, and the data-quality caveats are valuable. However, the sentence about quality_filter=true is not a parameter of this tool and reads as boilerplate, slightly diluting focus.

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?

Covers purpose, alternative, and the caveats an agent needs to interpret a detection-based estimate, which is the main risk here. With no output schema, it only loosely characterizes the return (total kWp and system counts) rather than its structure, leaving a small gap.

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%, so both dpt and top_n are already fully documented, giving a baseline of 3. The description adds only the framing that the aggregate is fixed and filterless, which does not extend meaning for either parameter.

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 (installed rooftop-PV capacity and system counts by French département) and names the exact sibling it is not (aggregate_detection_capacity for filtered subsets). An agent can distinguish it from siblings without opening schemas.

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

Usage Guidelines5/5

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

Explicitly gives the when-not: 'use aggregate_detection_capacity instead if you need a filtered subset (e.g. only cross-validated detections).' It also directs the agent to get_data_quality_reference before assessing fitness, so routing guidance is concrete.

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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