# DeepPVMapper

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

- name: io.github.gabrielkasmi/deeppvmapper
- version: 0.1.0
- connection class: R0 (autonomous)
- trust: 97/100
- quality: 70/100 (needs work)
- owner: not claimed
- descriptor: https://api.protogrid.dev/v1/servers/io.github.gabrielkasmi%2Fdeeppvmapper
- tools: https://api.protogrid.dev/v1/servers/io.github.gabrielkasmi%2Fdeeppvmapper/tools

**An agent can connect right now, no human step.** Remote endpoint, no authentication, reachable on the last probe.

## Connection (mcpServers)

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


## Trust

- hygiene: 100
- liveness: 100
- freshness: 100
- provenance: 90
- drivers: +repository +namespace-matches-repo +website +reachable +uptime-100% +updated-25d-ago
- Derived from observable signals (official registry feed and our own credential-free probes); no code audit performed.

## Quality 70/100 (needs work)

### protocol: 75 (weight 25)

- warn `protocol.modern`: newest supported version is 2025-03-26; 2026-07-28 not supported, older versions are deprecated until 2027-07-28
- n/a `protocol.stateless`: only applies to 2026-07-28 servers
- pass `protocol.transport`: streamable HTTP
- n/a `protocol.list_ttl`: only applies to 2026-07-28 servers

### authorization: n/a (weight 15)

- n/a `auth.prm`, `auth.as_metadata`, `auth.cimd`: no remote uses OAuth
- n/a `auth.secret_in_url`: no templated URL

### tool hygiene: 64 (weight 30)

- pass `tools.descriptions`: every tool has a description
- warn `tools.description_length`: 1 tools have descriptions over 1,024 characters, which crowds the context
- pass `tools.schemas`: every tool has an object input schema
- fail `tools.annotations`: no tool declares readOnlyHint or destructiveHint, so clients cannot tell safe calls from risky ones
- fail `tools.directory_hints`: no tool declares all four of readOnlyHint, destructiveHint, idempotentHint and openWorldHint; missing: get_data_quality_reference (readOnlyHint, destructiveHint, idempotentHint, openWorldHint); get_department_yearly_stats (readOnlyHint, destructiveHint, idempotentHint, openWorldHint); get_community_activity_in_area (readOnlyHint, destructiveHint, idempotentHint, openWorldHint); …
- pass `tools.token_cost`: about 7,750 tokens to load every tool
- pass `tools.api_dump`: tools are not a one-to-one API dump

### stability: 75 (weight 15)

- warn `stability.changes`: 14 tool changes in 30 days
- pass `stability.rug_pull`: no tool changed its meaning under the same name

### dependencies: n/a (weight 15)

- n/a `deps.known_vulns`, `deps.mcp_sdk_version`, `deps.resolvable`: no npm or PyPI package

- tools: 17, about 7,750 tokens to load them all
- tool set hash: 1e86a3ca45aaf5bf218988cad1b163b0
- tools last changed: 2026-10-18T23:37:51.752Z
- badge: [![protogrid quality](https://protogrid.dev/badge/io.github.gabrielkasmi/deeppvmapper.svg)](https://protogrid.dev/servers/io.github.gabrielkasmi/deeppvmapper)
- Checks run on what our credential-free, read-only probes observe; tools are never called and no code audit is performed.

### Tool changes

- 2026-10-18 `get_started` added
  - 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.
- 2026-10-18 `get_source_reference` added
  - 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 correction?". The `sources` field answers "where do we have this installation?": each listed code is a dataset or process that contains it. Example: "0,1" = present in both DeepPVMapper (DPVM) and FRPV; "0,2" = present in DeepPVMapper and OpenStreetMap.
- 2026-10-18 `get_field_reference` added
  - 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.
- 2026-10-18 `get_field_distribution` added
  - 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/last_seen are imagery years, not installation dates. 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.
- 2026-10-18 `get_field_coverage` added
  - 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 or one département. For a single filtered count use count_detections instead. Null means "not available/recorded", never "0" or "confirmed ok". 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.
- 2026-10-18 `get_department_profile` added
  - 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 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.
- 2026-10-18 `get_detection_details` added
  - 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 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.
- 2026-10-18 `get_source_breakdown` added
  - 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 how many detections are identified by 2+ sources (the meaning of quality_filter). 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.
- 2026-10-18 `count_detections` added
  - 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 OpenStreetMap?" -> sources_include=[2]. Returns the matching count, the unfiltered registry total and the percentage. Counts are DETECTED systems, not an inventory. 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.
- 2026-10-18 `get_detections_in_bbox` changed (description)
  - before: 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 no PV installation exists there. kwp, surface, tilt and azimuth are model estimates, not surveyed values. Call get_data_quality_reference for the full picture before assessing fitness for a specific use case.
  - after: 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 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.

Full history: https://api.protogrid.dev/v1/servers/io.github.gabrielkasmi%2Fdeeppvmapper/changes

## Remotes

- https://zelhliylrlktnasircwp.supabase.co/functions/v1/mcp/mcp (streamable-http, auth none, reachable true, uptime30d 1)

## Packages

_none_

## Tools (17)

- `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_department_capacity_stats, which is a fixed pre-computed département-wide aggregate with no other filters, this tool sums a live filtered subset, up to max_rows detections. Example: "installed capacity in Gironde confirmed by at least two sources" -> dpt="33", cross_validated=true. 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.
- `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 OpenStreetMap?" -> sources_include=[2]. Returns the matching count, the unfiltered registry total and the percentage. Counts are DETECTED systems, not an inventory. 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.
- `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 from this tool. This reflects unmoderated, unverified user activity, not the registry itself — do not present it as confirmed detection data.
- `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 bounding box (min_lon/min_lat/max_lon/max_lat) finds pending new additions (action=add) whose proposed footprint centroid falls inside it. Combine with get_department_capacity_stats / search_detections / get_detections_in_bbox for the confirmed registry picture, and present this separately and clearly as unverified, pending community input — not confirmed detection data. Free-text comments are never included.
- `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 terms. Call this before advising how much to trust a result for a specific use case (e.g. exploratory research vs. a commercial or regulatory decision) — pair it with the quality_summary attached to search_detections / aggregate_detection_capacity results, which reflects the specific query rather than the registry as a whole.
- `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. 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.
- `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 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.
- `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 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.
- `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 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.
- `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 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.
- `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 or one département. For a single filtered count use count_detections instead. Null means "not available/recorded", never "0" or "confirmed ok". 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.
- `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/last_seen are imagery years, not installation dates. 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.
- `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_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 how many detections are identified by 2+ sources (the meaning of quality_filter). 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.
- `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 correction?". The `sources` field answers "where do we have this installation?": each listed code is a dataset or process that contains it. Example: "0,1" = present in both DeepPVMapper (DPVM) and FRPV; "0,2" = present in DeepPVMapper and OpenStreetMap.
- `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.
- `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 for map/spatial queries with geometry). 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.