DeepPVMapper

Install

Pick the AI app you use, then copy the settings.

Passed 5/5 checks · What you ask goes through the maker’s server (zelhliylrlktnasircwp.supabase.co) · ✓ Commands checked Nov 13, 2026

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

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

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

Which apps can use it

Based on how this server connects and on each app’s own docs, checked Nov 10, 2026.

Connect over the internet in

ClaudeChatGPTGooseLM StudioMsty StudioCherry StudioCursorVS Code + GitHub CopilotVisual StudioWindsurf (Devin Desktop)ZedKiroClineContinueJetBrains AI AssistantClaude CodeCodex CLIGemini CLIOpenCodenodecodeWarpLibreChatOpen WebUIRaycast

All apps, with pros and cons →

What it can do

Tool names and descriptions exactly as the server reported them on Nov 14, 2026. The maker wrote them, not us; we show them as plain text.

• 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 l

• 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

• 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 m

• 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_detectio

• 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.

• 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 detec

• 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 inten

• 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 dép

• 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 co

• 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 to

• 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:

• 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

• 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 on

• 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 estim

• 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?", "w

• 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

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 15, 2026

• ✓ Pass

  Maker matches the repo

  The registry name was verified with the GitHub account gabrielkasmi, which owns the repo

• ✓ Pass

  Still maintained

  Code last changed 12 days ago

• ✓ Pass

  Has a licence

  Uses MIT

• ✓ Pass

  Endpoint responds

  Responded in 351 ms (HTTP 200)

• – Not applicable

  Sign-in with OAuth

  No sign-in needed

• ✓ Pass

  No hidden instructions in tool descriptions

  Read 17 tool descriptions and found no hidden instructions to the AI

What we check → · JSON

User reviews

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Compared with others in Otheri

• More stars than 96% of the other 19871 servers in this category
• Published to the registry more recently than 47% of the other 19871 servers in this category
• Basic checks equal to or better than 100% of the other 19871 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

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Same category, other makers, commands already checked. Official ones first, then by stars.

Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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README from the maker

No README found in the repo