San Diego City GIS

License: MIT Python 3.11+ MCP Compatible

---

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 the centroid + suggested_radius_km that bridge to eBird
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__get_iba_boundary	The site's actual polygon (WGS84 rings + bbox), for when inside the IBA has to mean inside rather than near
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.

Chaining to eBird — read this before you do

The obvious mashup ("which designated species are being seen here right now?") has two traps that return confident, silently wrong answers. Both are handled by the server, but only if you use it as intended:

• Never pass species_code to eBird. Audubon's taxonomy is frozen pre-2014, and most of its codes happen to coincide with eBird's — which is exactly what makes the rest dangerous. Site 202 is designated for "Clapper Rail"/clarai, but clarai is eBird's code for an Atlantic bird absent from California; the bird actually there is Ridgway's Rail (ridrai1). eBird answers "no records" and its absence-of-evidence disclaimer makes the zero look real. Resolve every bird through eBird's taxonomy by scientific name first.
• ebird_link is not a join key. It is a human-facing web URL; the US-CA_202 inside it is an Audubon id that every eBird tool rejects. The working bridge is the centroid plus the suggested_radius_km that get_iba_details returns (eBird's 25 km default is metro-scale against a site of a few km²). For a true in-boundary audit rather than a radius, use get_iba_boundary and filter hotspot coordinates against the rings.

Full details, including the probed classification of every stale code: docs/EBIRD_COMPOSABILITY.md.

Connect to the server

Add it as a custom connector in Claude (same steps on Claude.ai and Claude Desktop):

1. Settings → Connectors (or Customize → Connectors on claude.ai)
2. Add custom connector
3. Name it e.g. Audubon IBA and paste your deployment's /mcp URL.

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 eleven more checks against any deployment — including that species records still carry the eBird taxonomy warning, and that a site whose name contains a SQL keyword (Union) is still searchable:

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.

Credits & thanks

This server is a fork of OpenContext, created by Srihari Raman and released by the City of Boston, Department of Innovation and Technology under the MIT License.

Everything that makes this repository work — the plugin architecture, the MCP JSON-RPC server, the Lambda/HTTP adapters, the one-fork-one-server discipline, the deployment tooling — is theirs. This fork only adds a plugin for one bird-conservation dataset on top of that foundation, and the docs/ you'll find here (architecture, getting started, plugin development) are largely their work, retained as-is.

Publishing a civic data framework as open source, general enough that a bird-conservation dataset in Alaska can ride on a project built for Boston's open data, is a real public good. Thank you to Srihari Raman and to the City of Boston for building it and giving it away.

Thanks also to the National Audubon Society for publishing the Important Bird Areas data openly, and to the Cornell Lab of Ornithology for eBird, which this server is designed to compose with.

Licensed MIT — see LICENSE, which retains the original copyright.