arcgis-mcp-bridge

📦 Installation

You can install the official release of arcgis-mcp-bridge directly from PyPI (Python Package Index):

# Traditional installation
pip install arcgis-mcp-bridge
# Modern, lightning-fast alternative
uv pip install arcgis-mcp-bridge

100 declarative geoprocessing tools. Two isolated processes. One security floor.

A secure, local-first, asynchronous MCP server exposing ArcGIS Pro's ArcPy engine to Claude Desktop and other MCP hosts over stdio JSON-RPC.

Technical write-up: https://dev.to/muend/building-a-secure-mcp-bridge-for-arcgis-pro-and-arcpy-511g

Catalog	100 tools · 10 verticals
Tests	81 unit tests · 81/81 passing · arcpy mocked
Static analysis	Ruff clean · Mypy strict clean
Transport	JSON-RPC 2.0 over stdio
License	Apache-2.0

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Why arcgis-mcp-bridge?

Feature	arcgis-mcp-bridge	geo2004/MCP-ArcGISPro	nicogis (C#/.NET)
Tools	100	~15	~10
Dependency Sync	Deterministic (uv.lock)	Imperative (requirements.txt)	Native Nuget
Transport	stdio JSON-RPC	file-based IPC	Named Pipes
Security Architecture	Documented PathGuard sandbox	None specified / default host access	None specified / default host access
arcpy Isolation	Two-process architecture	Single process execution	Add-In in-process execution
CI (Offline Verification)	✅ Supported	❌ Not available	❌ Not available
License	Apache-2.0	MIT	MIT

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Highlight: Sketch → GIS Pipeline

Hand-drawn parcel boundary → photo → geodatabase feature class. ORB+RANSAC image registration, HSV ink segmentation, direct GDB commit. No manual digitizing required.

Demo coming soon. To preview the sketch-to-GIS pipeline:

1. Draw a polygon on paper and photograph it.
2. Ask Claude: "Use extract_sketch_to_gis to register this photo against my basemap and commit the result to my GDB."
3. The feature class appears in ArcGIS Pro — no manual digitizing.

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00 — Example Prompts

After health_check succeeds, talk to Claude naturally:

"Buffer all parcels in my GDB by 50 meters and save to scratch."
"List all feature classes in C:\GIS\city.gdb starting with 'road_'."
"Dissolve the neighborhoods layer by district_id."
"Run kernel density on crime_points with a 500-meter search radius."
"Calculate slope and aspect from the DEM at C:\GIS\dem.tif."
"Find the 3 nearest facilities to each incident in my network dataset."
"Check geometry on all feature classes in my GDB and repair errors."

01 — Core Architecture & Philosophy

flowchart TD
    A[Claude Desktop / Cursor] -->|JSON-RPC over stdio| B[Layer A · MCP Protocol Host]
    B -->|NDJSON subprocess bridge| C[Layer B · ArcPy Worker]
    C --> D[ArcGIS Pro / ArcPy Runtime]

Layer A — Async Event-Driven Server (arcgis_mcp/server.py). FastMCP on the bridge interpreter. Owns the stdio channel, validates every request against frozen Pydantic v2 contracts, dispatches work via asyncio.create_subprocess_exec — the event loop never blocks on a geoprocessing call and never holds a thread lock. Layer A contains zero module-level arcpy or cv2 imports (verified by grep in the audit gate); it cannot crash on Esri's native code because it never touches it.

Layer B — Subprocess ArcPy Isolation Worker (arcgis_mcp/worker.py). Spawned per job on the licensed ArcGIS Pro interpreter (ARCPY_PYTHON_PATH). The only place import arcpy is legal; cv2 loads lazily inside the one vision tool that needs it. Worker stdout is rebound to stderr at startup — the single sanctioned stdout write is the final NDJSON result frame, so native ArcObjects chatter can never corrupt the JSON-RPC channel. A native crash terminates the worker, not the server: the parent converts a non-zero exit into a structured error frame.

Declarative registry (arcgis_mcp/registry.py). Each tool is one ToolSpec(name, category, description, input_model, worker_fn, destructive). One generic proxy factory materializes all 100 MCP endpoints in Layer A; one generic run_tool dispatcher serves them in Layer B. Adding tool #101 touches two files — never the runtime loops.

Every failure crossing the process boundary is classified: validation · security · license · geoprocessing (with the full arcpy.GetMessages() stack) · internal.

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02 — The 100-Tool Census Matrix

#	Vertical	Tools	Key capabilities
1	map_layer_management	10	.aprx maps, layer order/visibility/symbology, camera, save
2	data_management	22	FC/GDB lifecycle, fields, Describe, Excel/GeoJSON/CSV exchange
3	geometry_analysis	23	Overlays, dissolve/merge, selections, joins, proximity, fishnet
4	coordinate_reference_projection	4	WKID-driven define/project for vector + raster, CRS lookup
5	raster_operations	15	Map algebra, zonal stats, DEM slope/aspect/hillshade, hydrology
6	vision_analytics	1	Sketch-to-GIS: ORB+RANSAC registration → HSV ink → GDB commit
7	export_layout	9	PDF/PNG plots, DPI control, map frames, text/legend, page size
8	editing_topology	7	Repair/check geometry, append, dedupe, diff, topology validation
9	network_analysis	4	Service areas, routing, OD cost matrix, closest facility
10	spatial_statistics	5	Mean center, ellipse, kernel density, Gi* hot spots, Moran's I
	Total	100

Esri extension licenses (Spatial, Network) are checked out through one shared context manager and checked back in inside finally — a crash can never leave a seat locked. Unavailable licenses return a structured frame, not a process drop.

Destructive Mutation Safety Floor

Ten state-mutating tools refuse to run without an explicit confirm: true payload token. The gate fires in the dispatcher before the 10–30 s arcpy import is paid, and the registry refuses to even register a destructive spec whose contract lacks a confirm field:

append_features        calculate_field        define_projection
delete_dataset         delete_field           delete_identical
extract_sketch_to_gis  near_analysis          remove_layer_from_map
repair_geometry

calculate_field carries an additional expression-channel floor: the default expression_type is ARCADE (Esri's sandboxed expression language), and PYTHON3 — which executes code inside the worker — is rejected at the Layer-A contract boundary unless confirm: true is explicitly supplied. raster_calculator expressions are constrained to a pure map-algebra grammar (identifiers, numbers, operators; no quotes, no dunder access) by a contract validator.

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03 — Automated Quality Gate & Testing

Scope, stated plainly: the automated gate currently consists of 81 unit tests spanning the PathGuard boundary, the Pydantic contracts, the generic registry path-guard and registration invariants, the worker's error-boundary mapping, and Settings environment validation. It exercises the catalog's structural contracts and every security-critical seam — it does not claim multi-scenario validation of the 100 geoprocessing tools themselves, which execute against a licensed ArcGIS runtime that no CI runner has.

In-memory test architecture. tests/conftest.py injects MagicMock proxies into sys.modules["arcpy"] and sys.modules["arcpy.sa"] (with CheckExtension answering "Available") before any package import resolves. The entire suite executes in well under a second, with no ArcGIS installation, no license checkout, and no Esri runtime — locally and in CI identically.

Test scopes.

• tests/test_security.py & tests/test_pathguard.py — the PathGuard boundary firewall, exercised against real directories via pytest's tmp_path fixture: valid reads/writes inside the sandbox pass; traversal (..-segments), UNC, relative, NUL-byte, reserved-device, over-length and out-of-root paths are rejected; write discipline (ArcGIS dataset-name rules, overwrite opt-in) is enforced. 30 tests.
• tests/test_contracts.py — Pydantic contract enforcement: per-tool parameter specs, cross-field validators, frozen / extra="forbid", and the ok-xor-error invariant on the IPC envelope. 15 tests.
• tests/test_registry.py & tests/test_registry_guard.py — registry stream integrity plus generic apply_path_guard enforcement and register invariants — every schema must be a ToolInput subclass, every path_fields entry must reference a valid role, duplicate names are rejected, and every destructive spec must carry its confirm gate. 11 tests.
• tests/test_worker.py — process_frame error-boundary mapping: every failure class (validation, security, license, geoprocessing, internal) maps to its distinct WorkerError.kind. 10 tests.
• tests/test_config.py — Settings.from_environment validation: required variables, directory/file checks, integer bounds, and the fail-fast on a missing scratch geodatabase. 15 tests.

The side-effect import import arcgis_mcp.tools in the registry test is what populates the catalog; it is # noqa-pinned so no linter ever strips it again.

Static analysis. Ruff enforces canonical formatting plus E/W/F/I/B/RUF at 88 columns against a py311 floor (code must parse on the oldest supported interpreter — Layer B). Turkish comments are first-class: the dotless ı/İ are registered under allowed-confusables, so prose is configured around, never rewritten. Mypy runs strict = true with the Pydantic plugin across all 31 source files.

make format          # ruff format + import sorting (mutates)
make lint            # ruff check, mutates nothing
make type-check      # mypy --strict over arcgis_mcp/
make security-audit  # live registry inspection: path roles + confirm gates
make verify-all      # lint + type-check + security-audit, one gate
python -m pytest     # 81/81

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04 — Security Framework (PathGuard Sandbox)

Every filesystem argument in every contract declares its role — "read", "write", or "read_list" — in the model's path_fields mapping. One shared enforcement function applies those declarations in both processes: Layer A pre-checks before a worker is ever spawned; Layer B re-validates because it never trusts its parent.

Two boundary controls:

• validate_read(raw: str) — fully resolves the path (symlinks, .., relative segments collapsed before any comparison) and requires containment inside a configured allowed_roots directory. Existence is enforced via a deepest-existing-prefix resolution strategy: the targeted path or its filesystem-resolvable geodatabase prefix must exist. This is what makes GDB-internal datasets (…\city.gdb\roads) first-class — the .gdb container is validated on the filesystem, while the logical tail is constrained to plain dataset names only arcpy can resolve.
• validate_write(raw: str, *, overwrite: bool) — same resolution and containment, plus ArcGIS-legal dataset naming and the overwrite discipline: an existing target is never replaced unless the request explicitly sets overwrite: true.

Any escape pattern — traversal sequences, UNC shares, NUL bytes, reserved device names, out-of-root targets — raises PathSecurityError immediately: the request is answered with a structured security frame and no subprocess is ever orchestrated for it.

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05 — 📦 Installation

Choose the onboarding pipeline that fits your operational objective:

Path A: Pure PyPI Installation (Recommended for Quick Deployments)

Ideal if you want to use the server out-of-the-box via Claude Desktop without cloning source files.

pip install arcgis-mcp-bridge
# Execute the unified setup console command to clone your environment
arcgis-mcp-setup

Path B: Git Clone & Deterministic Development (Recommended for GIS Contributors)

This project leverages Astral uv for light-speed, deterministic python environment management and synchronization.

# 1. Clone the repository
git clone https://github.com/muend/arcgis-mcp-bridge.git
cd arcgis-mcp-bridge

# 2. Create an ISOLATED dev venv pinned to the ArcGIS Pro 3.11 interpreter.
#    Do NOT pass --system-site-packages: Layer A never imports arcpy (the
#    worker runs in a SEPARATE interpreter resolved via ARCPY_PYTHON_PATH), so
#    inheriting ArcGIS's full site-packages only leaks interpreter-incompatible
#    third-party packages into the pytest/mypy gates. Keep this venv hermetic.
uv venv --python "C:\Program Files\ArcGIS\Pro\bin\Python\envs\arcgispro-py3\python.exe"

# 3. Activate the virtual environment
.venv\Scripts\activate

# 4. Sync all frozen dependencies deterministically using uv
#    --locked fails fast if uv.lock has drifted from pyproject.toml,
#    guaranteeing the install matches the committed resolution exactly.
uv sync --locked

Note: To enable the hand-drawn sketch-to-GIS pipeline, install using the [vision] or [dev,vision] flag to pull the downstream dependencies opencv-python-headless and numpy into your environment: uv sync --locked --extra vision or uv sync --locked --all-extras

Both paths share the same setup engine (arcgis-mcp-setup ≡ python -m arcgis_mcp.setup_env): idempotent, accepts --env-name (default arcgis-mcp-env) and --dry-run; set ARCGIS_CONDA_EXE if conda is not on PATH. It emits a JSON report whose python_exe value becomes ARCPY_PYTHON_PATH.

Worker integrity — ARCPY_PYTHON_PATH must resolve the package stack. Layer B is launched as -m arcgis_mcp.worker, so its interpreter must resolve the worker's runtime requirements — Pydantic above all (the IPC contracts are re-validated inside Layer B). The pristine arcgispro-py3 environment does not ship Pydantic and is read-only, so it cannot acquire it. Recommended configuration: point both the server command and ARCPY_PYTHON_PATH at the same cloned arcgis-mcp-env — one environment, one dependency set, no context drift, no missing-package failures at job time.

Install the full stack into that environment (pip install "pydantic>=2.5" mcp and, for the vision pipeline, pip install opencv-python-headless numpy).

Variable	Required	Purpose
ARCPY_PYTHON_PATH	yes	Layer B interpreter: licensed arcpy and Pydantic resolvable (use arcgis-mcp-env)
ARCGIS_MCP_ALLOWED_ROOTS	no	;-separated PathGuard boundary roots; defaults to ~/Documents/ArcGIS/Projects if unset
ARCGIS_MCP_SCRATCH_GDB	no	Default output workspace; must already exist (startup fails fast if missing)
ARCGIS_MCP_LOG_FILE / _LOG_LEVEL / _TOOL_TIMEOUT	no	Logging + per-job ceiling
ARCGIS_MCP_MAX_WORKERS	no	Concurrent arcpy worker ceiling (default 2) — protects license seats and RAM

Claude Desktop Configuration

Pick the block that matches how you installed the server. (ARCPY_PYTHON_PATH is required in both variants — it is the licensed worker interpreter reported by the setup command's JSON output.)

Option 1: Global/PyPI Installation Config

{
  "mcpServers": {
    "arcgis-mcp-bridge": {
      "command": "arcgis-mcp-server",
      "env": {
        "ARCPY_PYTHON_PATH": "C:\\...\\envs\\arcgis-mcp-env\\python.exe",
        "ARCGIS_MCP_ALLOWED_ROOTS": "C:\\GIS\\Data;C:\\Workspace",
        "ARCGIS_MCP_MAX_WORKERS": "2"
      }
    }
  }
}

Option 2: Local Git Clone Config

{
  "mcpServers": {
    "arcgis-mcp-bridge": {
      "command": "C:\\...\\envs\\arcgis-mcp-env\\Scripts\\python.exe",
      "args": [
        "-m",
        "arcgis_mcp.server"
      ],
      "env": {
        "PYTHONPATH": "C:\\path\\to\\arcgis-mcp-bridge",
        "ARCPY_PYTHON_PATH": "C:\\...\\envs\\arcgis-mcp-env\\python.exe",
        "ARCGIS_MCP_ALLOWED_ROOTS": "C:\\GIS\\Data;C:\\Workspace",
        "ARCGIS_MCP_MAX_WORKERS": "2"
      }
    }
  }
}

After restart, call health_check first — it proves the full server→worker pipeline without importing arcpy.

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06 — Compatibility

ArcGIS Pro	Python (arcgispro-py3)	Status
3.1	3.9	✅ Tested
3.2	3.9	✅ Tested
3.3	3.11	✅ Tested — reference platform
3.4	3.11	⚠ Community-reported, not CI-verified

Windows only. ArcPy is Windows-exclusive. Layer A runs on any platform for development (MagicMock injection), but Layer B requires a licensed ArcGIS Pro installation on Windows.

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07 — License

Apache License 2.0. See LICENSE.

<!-- mcp-name: io.github.muend/arcgis-mcp-bridge -->