MCP server

DeepPVMapper

io.github.gabrielkasmi/deeppvmapper

Checked 25 Oct 2026, 20:29 UTC · Connected to it and listed its tools

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

Website Source

At a glance

Type

MCP server

Publisher

Not verified

Status

Working at last check

Tools

17

Token cost

About 9,069 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

25 Oct 2026, 20:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http deeppvmapper https://zelhliylrlktnasircwp.supabase.co/functions/v1/mcp/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.deeppvmapper]
url = "https://zelhliylrlktnasircwp.supabase.co/functions/v1/mcp/mcp"

Gemini CLI

{"mcpServers":{"deeppvmapper":{"httpUrl":"https://zelhliylrlktnasircwp.supabase.co/functions/v1/mcp/mcp"}}}

Cursor

{"mcpServers":{"deeppvmapper":{"url":"https://zelhliylrlktnasircwp.supabase.co/functions/v1/mcp/mcp"}}}

VS Code

{"servers":{"deeppvmapper":{"type":"http","url":"https://zelhliylrlktnasircwp.supabase.co/functions/v1/mcp/mcp"}}}

Antigravity

agy mcp add deeppvmapper https://zelhliylrlktnasircwp.supabase.co/functions/v1/mcp/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"deeppvmapper","server_url":"https://zelhliylrlktnasircwp.supabase.co/functions/v1/mcp/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"deeppvmapper","server_url":"https://zelhliylrlktnasircwp.supabase.co/functions/v1/mcp/mcp"}

Tools

• aggregate_detection_capacity ~1639 tokens
  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
• count_detections ~1557 tokens
  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_community_activity ~231 tokens
  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 ~544 tokens
  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
• get_data_quality_reference ~233 tokens
  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 ~370 tokens
  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_profile ~298 tokens
  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_department_yearly_stats ~266 tokens
  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
• get_detection_details ~314 tokens
  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_detections_in_bbox ~358 tokens
  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_field_coverage ~345 tokens
  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 ~570 tokens
  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 ~122 tokens
  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_breakdown ~329 tokens
  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_source_reference ~254 tokens
  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 ~136 tokens
  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.
• search_detections ~1503 tokens
  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

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