San Diego City GIS MCP
Enables querying and spatial analysis of City of San Diego GIS layers (zoning, habitats, land use, etc.) via ArcGIS REST services, with tools for search, metadata, and spatial queries.
README
OpenContext
<p align="center"> <img src="docs/opencontext_logo.png" alt="OpenContext Logo" width="400"> </p>
Audubon IBA MCP — a National Audubon Society Important Bird Areas fork of OpenContext (forked from the San Diego City GIS fork). It serves Audubon's public IBA feature service through the audubon_iba plugin.
Where the sibling GIS forks front a whole portal or services directory, this one fronts one fixed FeatureServer with a known schema. There is nothing to crawl, so the catalog-discovery machinery is gone: no search_datasets, no get_aggregations, no catalog manifest, no dataset-id resolution. What carries over is the ArcGIS REST plumbing — WGS84 handling, coded-value domain decoding, pagination, and error surfacing.
An IBA is a conservation priority, not a legal status. IBA designation is science-based (BirdLife/Audubon criteria, ranked Global / Continental / State). It carries no legal or regulatory force: it does not protect land, restrict its use, or trigger permitting, and the boundaries are advisory rather than authoritative property lines. This server states that in its MCP
instructions, in its tool descriptions, and in a footer on its output.
The data source
https://services1.arcgis.com/lDFzr3JyGEn5Eymu/arcgis/rest/services/iba_polygons_public/FeatureServer
layer 0 iba_polygons_public -- the main IBA polygon record
tables 3 criteria_site 4 criteria_species 5 habitat 6 landuse
7 observation 8 ownership 9 site_description
10 species 11 threat
Public and key-less. Layer 0's fields, coded-value domains, record cap (2000), and relationship ids are read from the service at runtime (?f=json) and cached — nothing is hardcoded beyond the fields the tools actually name.
Sites are addressed by site_id (e.g. 1004 = Anchorage Coastal), which you get from search_ibas or find_ibas_near_point.
The WGS84 contract
The polygons are authored in EPSG:3857 (Web Mercator). Every query sets inSR=4326 and outSR=4326, so all tools take and return WGS84 lat/lng and the service reprojects server-side. Without inSR, a WGS84 point would be read as Web Mercator metres and silently match nothing. find_ibas_near_point adds a server-side buffer (distance + units=esriSRUnit_Kilometer) rather than computing geometry client-side.
Pagination is metadata-driven: maxRecordCount is read from the service, and both /query and /queryRelatedRecords page with resultOffset/resultRecordCount.
Tools exposed
| Tool | Purpose |
|---|---|
audubon_iba__find_ibas_near_point |
"Which IBAs are near here?" — buffers a WGS84 lat/lng by radius_km and returns every IBA polygon it intersects |
audubon_iba__search_ibas |
Attribute search: state (name or two-letter code), priority (Global/Continental/State), flyway, name (case-insensitive substring). Output leads with TOTAL MATCHING, so "how many IBAs in X?" needs no paging |
audubon_iba__get_iba_details |
The full polygon record for one site_id, leading with ebird_link |
audubon_iba__get_iba_related_records |
The tables behind a site, by category: species, criteria, criteria_species, habitat, landuse, ownership, threat, observation, description |
audubon_iba__list_distinct_values |
Cheap enum discovery for a field (flyway, iba_priority, iba_eba, iba_status, …) so you can filter on an exact value instead of guessing |
Workflow: fetch the polygon record first, then pull a related table only when the question needs one — species/criteria for why it was designated, threat/ownership/landuse for stewardship context, habitat for ecosystem, observation for history.
ebird_link is the integration point. It pre-joins each IBA to its eBird hotspot/region. For recent or live sightings, surface that link (or chain to an eBird MCP server) rather than reporting the stale observation table.
Connect to the server
Add it as a custom connector in Claude (same steps on Claude.ai and Claude Desktop):
- Settings → Connectors (or Customize → Connectors on claude.ai)
- Add custom connector
- Name it e.g.
Audubon IBAand paste your deployment's/mcpURL.
Quick health check from a terminal:
curl -sS -X POST http://localhost:8000/mcp \
-H 'Content-Type: application/json' \
-d '{"jsonrpc":"2.0","id":1,"method":"ping"}'
# → {"jsonrpc":"2.0","id":1,"result":{"status":"ok"}}
Raw JSON-RPC example:
curl -sS -X POST http://localhost:8000/mcp \
-H 'Content-Type: application/json' \
-d '{"jsonrpc":"2.0","id":1,"method":"tools/call",
"params":{"name":"audubon_iba__find_ibas_near_point",
"arguments":{"lat":61.17,"lng":-149.9,"radius_km":40}}}'
Verified end-to-end
The definition-of-done is a two-step chain around Anchorage (61.17, -149.9): a 40 km buffer search, then a related-record traversal on a site it returns. Together they prove the WGS84 buffer contract and the site_id → objectid → queryRelatedRecords chain (the relationship id is not the related table's id, and queryRelatedRecords keys on objectid, not site_id).
// audubon_iba__find_ibas_near_point
{ "lat": 61.17, "lng": -149.9, "radius_km": 40 }
returns six Alaska IBAs — Anchorage Coastal, Campbell Creek, Goose Bay, Palmer Hay Flats, Susitna Flats, Swanson Lakes — each with its priority rank and ebird_link. Traversing one of them:
// audubon_iba__get_iba_related_records
{ "site_id": 1004, "category": "species" }
returns the birds Anchorage Coastal was designated for (Short-billed Dowitcher, Snow Goose, Hudsonian Godwit, Sandhill Crane).
scripts/smoke_prod.py runs both plus eight more checks against any deployment:
python scripts/smoke_prod.py # production
python scripts/smoke_prod.py http://localhost:8000/mcp # local
Data use
The endpoint is public, but Audubon routes formal reuse of the spatial data through a request process. Ad-hoc display and analysis in conversation is fine; anything beyond that should go through Audubon.
Local development
uv sync # or: pip install -r requirements.txt
python scripts/local_server.py # serves http://localhost:8000/mcp
python -m pytest tests/ -q # tests
On Windows, set PYTHONIOENCODING=utf-8 before local_server.py (it prints emoji).
See CLAUDE.md and docs/ for architecture, deployment, and plugin development.
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