# Gee Mcp

> MCP server exposing Google Earth Engine as a set of MCP tools. These include: dataset discovery, metadata extraction, analysis primitives, and AI-assisted GEE Python code generation.

- **Type:** MCP server
- **Install:** `agentstack add mcp-frontierdevelopmentlab-gee-mcp`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [FrontierDevelopmentLab](https://agentstack.voostack.com/s/frontierdevelopmentlab)
- **Installs:** 0
- **Category:** [Integrations](https://agentstack.voostack.com/c/integrations)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [FrontierDevelopmentLab](https://github.com/FrontierDevelopmentLab)
- **Source:** https://github.com/FrontierDevelopmentLab/gee-mcp

## Install

```sh
agentstack add mcp-frontierdevelopmentlab-gee-mcp
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# GEE MCP

[](https://github.com/FrontierDevelopmentLab/gee-mcp/actions/workflows/main.yml)
[](https://github.com/FrontierDevelopmentLab/gee-mcp)
[](LICENSE)

An [MCP](https://modelcontextprotocol.io/) server that exposes
[Google Earth Engine](https://earthengine.google.com/) (GEE) as a set
of MCP tools covering dataset discovery, metadata extraction, analysis
primitives, and AI-assisted GEE Python code generation.

GEE-MCP is part of an initiative by [Trillium Technologies](https://trillium.tech/) and ESA to 
realize the vision of Earth system predictability (ESP).
You can read about the ESP vision [here](https://eslab.ai/esp). 

## Architecture

## Tools

The server registers the following MCP tools, grouped by purpose.

### Catalogue & Metadata
- `list_datasets`: list all available Google Earth Engine datasets.
- `get_dataset_info`: get detailed Markdown information about a GEE
  dataset.
- `get_dataset_metadata`: get structured STAC metadata (bands,
  temporal interval, etc.) for a dataset.
- `check_imagery_availability`: check imagery availability for a
  dataset within a date range and optional bounding box.
- `extract_metadata`: extract structured metadata (bands, pixel
  size, availability, cadence) from a dataset page.
- `analyze_metadata`: use Gemini to analyse a dataset description
  and extract structured metadata.

### Analysis & Data Processing
- `download_satellite_image`: download satellite images from GEE.
- `compute_index`: compute a spectral index (NDVI, NDWI, …) or a
  custom band-math expression over a region.
- `zonal_statistics`: compute summary statistics (mean, median,
  min, …) for bands or an index within a region.
- `temporal_composite`: create cloud-free temporal composites
  (median, mosaic, greenest, most recent).
- `mask_by_raster`: apply a value-range mask (DEM, land cover, …)
  to imagery and compute statistics.
- `threshold_area`: compute the area of pixels meeting a threshold
  condition on a band, index, or expression.
- `multi_period_analysis`: run the same analysis across multiple
  date ranges for temporal comparisons.
- `execute_gee_python`: execute a provided GEE Python script and
  return the result.

### AI Code Generation & Validation
- `generate_python_from_question`: answer an Earth Observation
  question by generating GEE Python code with iterative error fixing.
- `generate_abstract_graph_from_question`: generate an abstract
  Mermaid graph describing an EO pipeline that solves a question.
- `generate_python_from_reasoning_steps`: generate GEE Python code
  from a provided set of reasoning steps.
- `generate_python_from_abstract_graph`: generate GEE Python code
  from a provided Mermaid graph.
- `get_datasets_locations_and_periods`: determine the GEE datasets,
  time periods, and AOIs required to answer a question.
- `extract_factuality_issues`: analyse a GEE Python script and
  surface scientific assumptions worth verifying.
- `assess_factuality_issue`: produce an expert-style assessment of
  a factuality issue, with optional code-fix recommendations.
- `identify_sensible_variables`: identify variables and constants
  in the code whose values might affect the final result.
- `sensitivity_analysis`: perform sensitivity analysis by tweaking
  variable values and plotting the impact on the final result.

## Example tool invocation

A JSON-RPC call to `generate_python_from_question` looks like:

```json
{
  "method": "tools/call",
  "params": {
    "name": "generate_python_from_question",
    "arguments": {
      "question": "Calculate the average NDVI over the Amazon basin for the year 2023.",
      "fix_code": true
    }
  }
}
```

The response includes:

- `python_code`: the generated GEE Python code,
- `python_code_explanation`: an explanation of the code,
- `python_code_fix_history`: the iterative fixes attempted,
- `python_code_result`: the result of executing the code.

The generated code defines `gee_main()` returning `(result_xml, Map)`,
where `result_xml` follows a `...
......` shape,
and `Map` is a `geemap.Map` for notebook display.

## Installation

The project uses [Poetry](https://python-poetry.org/).

```bash
git clone https://github.com/FrontierDevelopmentLab/gee-mcp.git
cd gee-mcp
poetry install
```

Supported Python versions: 3.11–3.14.

## Configuration

You need to configure access to an **LLM provider** and to **Google Earth
Engine** via environment variables. Copy [`.env.example`](.env.example)
to `.env` and fill in the values; `python-dotenv` is loaded on server
startup.

### LLM provider

The server talks to an LLM through a small pluggable layer
(`gee_mcp.server.llm`). Pick a provider and model:

| Variable | Required | Purpose |
| --- | --- | --- |
| `LLM_PROVIDER` | yes | One of `google`, `anthropic`, `openai` |
| `LLM_NAME` | yes | Model id for that provider (e.g. `gemini-3.1-pro-preview`, `claude-opus-4-7`, `gpt-5`) |

Then set the credentials for the provider you chose:

**`google`** — either an API key:

| Variable | Required | Purpose |
| --- | --- | --- |
| `GEMINI_API_KEY` | yes | Gemini Developer API key |

`GOOGLE_API_KEY` is also accepted as a fallback for compatibility
with Google's official SDK convention.

…or a Vertex AI project (after running `gcloud auth
application-default login`):

| Variable | Required | Default | Purpose |
| --- | --- | --- | --- |
| `VERTEXAI_PROJECT` | yes |  | GCP project ID for Vertex AI |
| `VERTEXAI_LOCATION` | no | `global` | GCP region for Vertex AI |

**`anthropic`**:

| Variable | Required | Purpose |
| --- | --- | --- |
| `ANTHROPIC_API_KEY` | yes | Anthropic API key |

**`openai`**:

| Variable | Required | Purpose |
| --- | --- | --- |
| `OPENAI_API_KEY` | yes | OpenAI API key |

### Google Earth Engine

| Variable | Required | Purpose |
| --- | --- | --- |
| `GEE_PROJECT` | yes | GEE project ID |
| `GOOGLE_APPLICATION_CREDENTIALS` | no | Path to a service-account JSON key file. Only needed if you have not authenticated via `earthengine authenticate` or `gcloud`. |
| `GEE_SKIP_AUTH` | no | Set to `1` to skip auth entirely (used by the test suite). |

Authentication is delegated to `ee.Initialize`, which walks the
standard Google Cloud credential chain. Either run `earthengine
authenticate` (interactive, once per machine) or set
`GOOGLE_APPLICATION_CREDENTIALS` to a service-account key path. On
GCE / Cloud Run, instance metadata is picked up automatically.

## Running the server

Over stdio (the standard MCP transport):

```bash
poetry run python -m gee_mcp.server
```

## Testing with the example client

The repo includes `client.py`, an example MCP client that launches
the server as a subprocess.

```bash
poetry run python client.py
```

See `example.ipynb` for a notebook walk-through.

## Integration

GEE MCP can be plugged into any MCP-aware agent. For example, in
Gemini-CLI:

For the question:

> Characterize the morphometry and land cover of the Emme catchment
> in the Canton of Bern by determining its total area, maximum
> elevation, and forest cover percentage. Additionally, state the
> financial magnitude of the damages caused by the flash flood event
> in this catchment (specifically in Schangnau) during July 2014.

the generated code returns:

## Development

Install the pre-commit hooks once:

```bash
poetry run pre-commit install
```

Run the full hook suite (detect-secrets, autoflake, black, isort,
mypy, pylint, pytest+coverage):

```bash
poetry run pre-commit run --all-files
```

Run just the tests:

```bash
poetry run pytest
```

## Acknowledgements

Originally created by the GeoSTARS (STARS: Scientific Testing of Agentic Reasoning) team ([Raúl Ramos](https://github.com/rramosp), [James Walsh](https://github.com/dead-water), [Will Fawcett](https://github.com/will-fawcett), and [Russell Spiewak](https://github.com/r-spiewak), [Poliana Santana](https://github.com/psantana-trillium)). 
Supported by the ESA Phi-Lab as part of Trillium Technologies Earth Systems Lab [ESL](https://eslab.ai/).  

## License

MIT licensed. See [LICENSE](LICENSE) for the full text.

## Copyright

© 2026 Trillium Technologies Ltd.

## Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [FrontierDevelopmentLab](https://github.com/FrontierDevelopmentLab)
- **Source:** [FrontierDevelopmentLab/gee-mcp](https://github.com/FrontierDevelopmentLab/gee-mcp)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** yes
- **Environment & secrets:** yes
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/mcp-frontierdevelopmentlab-gee-mcp
- Seller: https://agentstack.voostack.com/s/frontierdevelopmentlab
- Browse the marketplace: https://agentstack.voostack.com/browse

---
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