MapSmith

# MapSmith

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**Professional-grade geoprocessing for AI agents — with provenance you can verify.**

MapSmith is an open-source [MCP](https://modelcontextprotocol.io) server that gives an AI
agent real GIS analysis — buffers, overlays, reprojections, zonal statistics, terrain and
hydrology — executed by GeoPandas, DuckDB Spatial, exactextract and Whitebox Workflows,
never written by the model. Every dataset it produces lands on disk next to a lineage
manifest: inputs with checksums, the exact parameters, the CRS decisions and *why*, engine
versions, and the deterministic checks that ran on the result.

> Ask for the result. The agent picks the tools. You can check the work afterwards.

Evidence before promises: an [A/B on GABench](docs/benchmarks.md) whose headline is a null
result — with the analysis that took our own positive number apart — [notebooks](examples/)
on a real USGS DEM of Mount St. Helens, and an
[in-chat map panel](#see-results-inside-the-chat) that shows the verification status of
every layer it draws.

## Quickstart

Add MapSmith to any MCP client over stdio (Claude Desktop, Claude Code, Cursor, VS Code):

```json
{
  "mcpServers": {
    "mapsmith": {
      "command": "uvx",
      "args": ["mapsmith"]
    }
  }
}
```

Docker is the supported path, and confines the server to the directory you mount:

```json
{
  "mcpServers": {
    "mapsmith": {
      "command": "docker",
      "args": ["run", "-i", "--rm",
               "-v", "/absolute/path/to/your/data:/data",
               "-e", "MAPSMITH_WORKSPACE=/data",
               "ghcr.io/mapsmith-ai/mapsmith"]
    }
  }
}
```

One-click installs:

[![Install in Cursor](https://cursor.com/deeplink/mcp-install-dark.svg)](https://cursor.com/install-mcp?name=mapsmith&config=eyJjb21tYW5kIjoidXZ4IiwiYXJncyI6WyJtYXBzbWl0aCJdfQ%3D%3D)
[![Install in VS Code](https://img.shields.io/badge/VS_Code-Install_MapSmith-0098FF?logo=visualstudiocode&logoColor=white)](https://insiders.vscode.dev/redirect/mcp/install?name=mapsmith&config=%7B%22command%22%3A%22uvx%22%2C%22args%22%3A%5B%22mapsmith%22%5D%7D)

or from a terminal: `code --add-mcp '{"name":"mapsmith","command":"uvx","args":["mapsmith"]}'`

To check it runs before wiring a client, `uvx mapsmith` starts the server on stdio
(Ctrl-C to quit) — it speaks MCP, not a CLI, so a silent prompt means it is working.

Then ask your agent things like:

> "Take parcels.gpkg, keep only the parcels within 300 m of the river in rivers.gpkg, and
> give me the result with the analysis lineage."

The Docker image includes the `[raster]` and `[whitebox]` extras. With `uvx`, pick your
own: `uvx --from "mapsmith[raster,whitebox]" mapsmith`. **Docker — or `uvx` on a machine
with working wheels — is the only supported installation path**: geospatial native
dependencies across three OSes are a support black hole, and issues about broken local
environments will be redirected here.

## What you get back

Every dataset comes with the file below, written next to it as
`<output>.provenance.json` — enough to re-run the analysis without the model that asked
for it:

```json
{
  "mapsmith_version": "0.2.1",
  "operation": "buffer_layer",
  "parameters": {"distance_meters": 300.0},
  "inputs": [{"path": "rivers.gpkg", "sha256": "9f2c…", "crs": "EPSG:4326"}],
  "crs_decisions": {"analysis_crs": "EPSG:32632", "reason": "estimated UTM zone for metric buffering"},
  "engine": {"name": "geopandas", "version": "1.0.1"},
  "started_at": "2026-08-18T10:15:03Z",
  "finished_at": "2026-08-18T10:15:04Z"
}
```

The full manifest also carries the verification checks that ran, and any geometry MapSmith
had to repair. `get_provenance` returns it for any output.

## Why MapSmith

- **Real geoprocessing, not map CRUD.** Built on the proven open geospatial stack: GDAL,
  GeoPandas, Shapely, DuckDB Spatial, Whitebox Workflows and exactextract ship today
  (more to come: PDAL, QGIS Processing via sidecar).
- **Provenance by design.** Every layer MapSmith produces ships with a machine-readable
  lineage manifest — source datasets with checksums, tools executed, exact parameters, CRS
  decisions, software versions, timestamps. Everything needed to re-run the analysis
  without the LLM is in there. No AI slop.
- **The engines compute, the model orchestrates.** Geometry and numbers only ever come
  from deterministic tool executions — never from model output.
- **Semantic tools, not a tool dump.** 16 goal-level tools plus a searchable operation
  catalog (progressive discovery), because agent accuracy collapses when you expose
  hundreds of raw tools.
- **Model-agnostic infrastructure.** Claude, GPT, Qwen, Kimi, GLM — anything that speaks
  MCP, cloud or local. The leverage is better contracts (typed plans, actionable error
  codes, a searchable catalog), not weights we would have to maintain. See
  [the manifesto](MANIFESTO.md).

## Tools

| Tool | What it does |
|---|---|
| `describe_dataset` | CRS, geometry types, schema, extent, feature count of any vector dataset |
| `buffer_layer` | Metric buffer with automatic UTM estimation for geographic CRS |
| `clip_layer` | Clip a layer with a mask layer |
| `reproject_layer` | Reproject to any CRS (EPSG code or WKT) |
| `spatial_join` | Join by spatial predicate, auto-routed to the fastest engine (SedonaDB > DuckDB > GeoPandas) |
| `run_sql` | Spatial SQL (DuckDB dialect) over GeoParquet and GDAL formats |
| `zonal_statistics` | Raster statistics per vector zone with exact fractional pixel coverage (`[raster]` extra) |
| `hillshade` | Shaded relief from a DEM, in-memory Whitebox engine (`[whitebox]` extra) |
| `flow_accumulation` | D8 flow accumulation with automatic depression filling (`[whitebox]` extra) |
| `watershed` | Watershed delineation from a DEM and pour points (`[whitebox]` extra) |
| `preview_map` | Interactive in-chat map (MCP Apps) of any datasets, with a provenance card and verification status per layer |
| `validate_plan` | Statically validate a multi-step plan before running anything: operations, arguments, references, input files, simulated CRS flow |
| `execute_plan` | Validate then run a plan step by step, with per-step provenance and a plan-level manifest |
| `get_provenance` | Return the full lineage manifest of any MapSmith output |
| `list_operations` | BM25-ranked catalog search; `detail=true` returns parameters and worked examples |
| `server_info` | Version, license, available engines |

## Verification, in and out

Every tool that writes a dataset also writes `<output>.provenance.json` beside it and
verifies its own work — CRS agreement, geometry validity, raster dimensions, count and
extent invariants — recording the results in the manifest *before* raising anything, so
the audit trail survives the error.

Verification runs on the way in as well. Before an operation touches your data, MapSmith
checks the failures that produce *plausible* junk: an input with no CRS is refused
outright, because metric maths on unknown units is how a confidently wrong answer gets
made; an empty input, or two layers whose extents cannot possibly overlap, comes back as
a named warning with a hint — in the tool result, not only in the manifest, so the agent
sees it instead of assuming success. (The join fast paths, DuckDB and SedonaDB, only ever
receive inputs that already share a known CRS; they verify their output and diagnose an
empty join.)

An output whose geometry is *mechanically* broken — typically invalidity inherited from an
invalid input — is repaired deterministically: `make_valid`, at most two rounds, written
to a temporary file and swapped in only once it is complete, and skipped rather than
risked where a rewrite could drop data (a multi-layer GeoPackage is refused, not
rewritten). Every attempt lands in the manifest *and* in the tool result, because a
repaired output must never look like one that was right the first time. Failures that need
judgement are never "fixed": an empty result, or geometries eroded away by a wrong
distance, come back as warnings with hints for the agent to act on.

## See results inside the chat

![MapSmith's interactive map panel rendered inside Claude Desktop: OSM basemap, buffer and zone layers, and per-layer provenance cards with verification status](docs/images/map-panel.png)

`preview_map` renders your layers on an interactive map panel *inside* the chat — pan,
zoom, toggle layers, and read each layer's provenance card (operation, engine, and one of
three honest states: `verified ✓`, `verification failed`, or `not verifiable` when no
critical check ran) right next to the geometry it explains. Field-tested on Claude
Desktop; it renders in any client that implements the official
[MCP Apps](https://modelcontextprotocol.io/extensions/apps/overview) extension, and on
clients without it the same call returns the preview as structured data.

The panel is self-contained — no CDN, no bundled libraries, no telemetry — with one
outbound request named here rather than buried: the OpenStreetMap background tiles, which
reveal the map view you are looking at (never your data) and which the panel drops to a
plain backdrop when the host blocks them. The preview is deliberately lossy (simplified
geometry, capped feature counts): the dataset of record stays on disk with its manifest.

## Plans: reject wrong analyses before they run

In [GISAgentBench](https://arxiv.org/abs/2608.01645) — 34