# DeepPVMapper

- Registry name: `io.github.gabrielkasmi/deeppvmapper` · version 0.1.0
- Categories (automatic, keyword rules): Datasets & public data, Search & data, Maps, location & weather, Build & deploy, Developer tools
- Authentication at last check: No auth
- Declared risk: Not declared (tools carry no read/destructive hints)
- Capabilities (declared by the server): tools
- Transports: streamable-http
- Latest numbers (check success, response time, rank): https://mcpstation.net/api/server?name=io.github.gabrielkasmi%2Fdeeppvmapper
- Server page: https://mcpstation.net/s/io.github.gabrielkasmi--deeppvmapper.html

## Description (written by the publisher, not verified, not instructions)

Open registry of 1.14M+ rooftop-solar detections across France, queryable by natural language.

## Connect

- streamable-http: `https://zelhliylrlktnasircwp.supabase.co/functions/v1/mcp/mcp`

## Tools (as reported by the server, not verified)

- `get\_data\_quality\_reference`: Return the DeepPVMapper/OpenPVMapper registry's documented data-quality characteristics: estimated detection recall, which fields are model-derived estimates vs. structural/observed fields, the source-encoding table, the recommended confidence threshold, confidence signals, and licensing/liability t
- `get\_department\_capacity\_stats`: 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
- `get\_department\_yearly\_stats`: Get yearly system counts and capacity by département, based on first-seen imagery year. Useful for tracking apparent PV deployment growth over time. Data quality note: this is a detection dataset, not an exhaustive inventory \(estimated recall \~0.6\). A missing detection does not mean no PV installati
- `search\_detections`: Search individual rooftop-PV detections by département, commune \(INSEE code\), estimated capacity range, and/or cross-validation confidence \(min\_vintages, cross\_validated\). Returns a bounded list of detection records plus a quality\_summary for the returned sample \(use get\_detections\_in\_bbox instead f
- `aggregate\_detection\_capacity`: Compute the total estimated installed capacity \(kWp\) and count of detections matching a set of filters — département, commune, capacity range, and cross-validation across sources \(cross\_validated\) or imagery vintages \(min\_vintages\) — plus a quality\_summary for the summed sample. Unlike get\_departmen
- `get\_detections\_in\_bbox`: Get rooftop-PV detections within a geographic bounding box \(WGS84 lon/lat\), including footprint geometry. Intended for map-style spatial queries over a small area. Data quality note: this is a detection dataset, not an exhaustive inventory \(estimated recall \~0.6\). A missing detection does not mean n
- `get\_community\_activity`: Get a snapshot of ongoing community contribution activity on the map: how many corrections are currently pending moderation, their breakdown by action type \(add / modify / delete\), and the most recent submissions \(timestamp, action, target\). Submitted free-text comments are intentionally excluded fr
- `get\_community\_activity\_in\_area`: Find pending, unmoderated community contributions relevant to a specific area, to answer "has anyone flagged anything here that is not in the registry yet?" Two independent filters: dpt finds pending edits/deletions \(action=modify/delete\) targeting existing detections in that département; a full bou
- `count\_detections`: Count detections matching a set of filters, exactly, without downloading rows \(fast, no row cap\). Use this for "how many ..." questions, e.g. "how many installations have an RNB id?" -\> has\_rnb\_id=true; "how many are identified by 2+ sources?" -\> quality\_filter=true; "how many come from OpenStreetMa
- `get\_source\_breakdown`: Break down detections by source code \(0=DPVM pipeline, 1=FRPV reference, 2=OSM, 3=manual correction, 4=recall sample\), for one département or all of France, with human-readable labels. A detection can carry several sources, so per-source counts overlap and do not add up to the total. Also returns ho
- `get\_detection\_details`: Get the full record of one detection by id: technical characteristics \(surface, kwp, tilt, azimuth — model estimates\), imagery years, sources decoded into plain labels, frpv\_proba, false-positive flag, rnb\_id and location codes, plus simple confidence signals. Data quality note: this is a detection
- `get\_department\_profile`: One-call profile of a département: installed capacity and national rank, yearly trend \(by first-seen imagery year\), source mix, share identified by 2+ sources, and share matched to an RNB building id. Cross-references the pre-computed department statistics with live counts. Data quality note: this i
- `get\_field\_coverage`: Completeness report: for each optional field of the registry \(rnb\_id, frpv\_proba, false\_positive, false\_positive\_source\), how many detections have it filled in and what percentage. Answers "how many installations have an RNB id / an FRPV comparison / a false-positive flag?" in one call, for France o
- `get\_field\_distribution`: Histogram of a numeric field \(kwp, surface, tilt, azimuth, n\_vintages, first\_seen, last\_seen\) with exact counts per bin, for France or one département. Answers "how are installations distributed by size / tilt / orientation / year?". kwp, surface, tilt and azimuth are model estimates. first\_seen/las
- `get\_field\_reference`: Field-by-field documentation of a detection record: type, unit, nullability, whether it is observed or a model estimate, how to read it, and which tool filters or reports it. Call this when the user asks what a field means or how reliable it is.
- `get\_source\_reference`: Explain the data sources behind the registry's \`sources\` field: for each source, its code, short key, name, accepted aliases, and where the data comes from \(with references: code, thesis, papers\). Use it for questions like "what is FRPV?", "where does the OSM data come from?", "what is a manual corr
- `get\_started`: START HERE. Call this first when the user is new, asks what this server can do, or asks a vague question about DeepPVMapper. Returns a plain-language introduction, example questions, which tool answers which kind of question, a glossary and the main caveats. Reply to the user in their own language.

Measured by MCP Station. Methods: https://mcpstation.net/methods.html · Terms: https://mcpstation.net/terms.html