# Open Streetmap Mcp

> An OpenStreetMap MCP server implementation that enhances LLM capabilities with location-based services and geospatial data.

- **Type:** MCP server
- **Install:** `agentstack add mcp-jagan-shanmugam-open-streetmap-mcp`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [jagan-shanmugam](https://agentstack.voostack.com/s/jagan-shanmugam)
- **Installs:** 0
- **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [jagan-shanmugam](https://github.com/jagan-shanmugam)
- **Source:** https://github.com/jagan-shanmugam/open-streetmap-mcp

## Install

```sh
agentstack add mcp-jagan-shanmugam-open-streetmap-mcp
```

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

## About

# OpenStreetMap (OSM) MCP Server

An OpenStreetMap MCP server implementation that enhances LLM capabilities with location-based services and geospatial data.

## Demo

### Meeting Point Optimization

### Neighborhood Analysis

### Parking Search

## Installation

### In MCP Hosts like Claude Desktop, Cursor, Windsurf, etc.
- `osm-mcp-server`: The main server, available for public use.
  
  ```json
  "mcpServers": {
    "osm-mcp-server": {
      "command": "uvx",
      "args": [
        "osm-mcp-server"
      ]
    }
  }
  ```

## Features

This server provides LLMs with tools to interact with OpenStreetMap data, enabling location-based applications to:

- Geocode addresses and place names to coordinates
- Reverse geocode coordinates to addresses
- Find nearby points of interest
- Get route directions between locations
- Search for places by category within a bounding box
- Suggest optimal meeting points for multiple people
- Explore areas and get comprehensive location information
- Find schools and educational institutions near a location
- Analyze commute options between home and work
- Locate EV charging stations with connector and power filtering
- Perform neighborhood livability analysis for real estate
- Find parking facilities with availability and fee information

## Components

### Resources

The server implements location-based resources:
- `location://place/{query}`: Get information about places by name or address
- `location://map/{style}/{z}/{x}/{y}`: Get styled map tiles at specified coordinates

### Tools

The server implements several geospatial tools:
- `geocode_address`: Convert text to geographic coordinates
- `reverse_geocode`: Convert coordinates to human-readable addresses
- `find_nearby_places`: Discover points of interest near a location
- `get_route_directions`: Get turn-by-turn directions between locations
- `search_category`: Find places of specific categories in an area
- `suggest_meeting_point`: Find optimal meeting spots for multiple people
- `explore_area`: Get comprehensive data about a neighborhood
- `find_schools_nearby`: Locate educational institutions near a specific location
- `analyze_commute`: Compare transportation options between home and work
- `find_ev_charging_stations`: Locate EV charging infrastructure with filtering
- `analyze_neighborhood`: Evaluate neighborhood livability for real estate
- `find_parking_facilities`: Locate parking options near a destination

## Local Testing

### Running the Server

To run the server locally:

1. Install the package in development mode:

```bash
pip install -e .
```

2. Start the server:

```bash
osm-mcp-server
```

3. The server will start and listen for MCP requests on the standard input/output.

### Testing with Example Clients

The repository includes two example clients in the `examples/` directory:

#### Basic Client Example

`client.py` demonstrates basic usage of the OSM MCP server:

```bash
python examples/client.py
```

This will:
- Connect to the locally running server
- Get information about San Francisco
- Search for restaurants in the area
- Retrieve comprehensive map data with progress tracking

#### LLM Integration Example

`llm_client.py` provides a helper class designed for LLM integration:

```bash
python examples/llm_client.py
```

This example shows how an LLM can use the Location Assistant to:
- Get location information from text queries
- Find nearby points of interest
- Get directions between locations
- Find optimal meeting points
- Explore neighborhoods

### Writing Your Own Client

To create your own client:

1. Import the MCP client:
```python
from mcp.client import Client
```

2. Initialize the client with your server URL:
```python
client = Client("http://localhost:8000")
```

3. Invoke tools or access resources:
```python
# Example: Geocode an address
results = await client.invoke_tool("geocode_address", {"address": "New York City"})
```

#### Claude Desktop config for local server

On MacOS: `~/Library/Application\ Support/Claude/claude_desktop_config.json`
On Windows: `%APPDATA%/Claude/claude_desktop_config.json`

  Development/Unpublished Servers Configuration
  
  ```json
  "mcpServers": {
    "osm-mcp-server": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/osm-mcp-server",
        "run",
        "osm-mcp-server"
      ]
    }
  }
  ```

## Development

### Building and Publishing

To prepare the package for distribution:

1. Sync dependencies and update lockfile:
```bash
uv sync
```

2. Build package distributions:
```bash
uv build
```

This will create source and wheel distributions in the `dist/` directory.

3. Publish to PyPI:
```bash
uv publish
```

Note: You'll need to set PyPI credentials via environment variables or command flags.

### Debugging

Since MCP servers run over stdio, debugging can be challenging. For the best debugging
experience, we strongly recommend using the [MCP Inspector](https://github.com/modelcontextprotocol/inspector).

You can launch the MCP Inspector via [`npm`](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm) with this command:

```bash
npx @modelcontextprotocol/inspector uv --directory /path/to/osm-mcp-server run osm-mcp-server
```

Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.

## Source & license

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

- **Author:** [jagan-shanmugam](https://github.com/jagan-shanmugam)
- **Source:** [jagan-shanmugam/open-streetmap-mcp](https://github.com/jagan-shanmugam/open-streetmap-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:** no
- **Environment & secrets:** no
- **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-jagan-shanmugam-open-streetmap-mcp
- Seller: https://agentstack.voostack.com/s/jagan-shanmugam
- Browse the marketplace: https://agentstack.voostack.com/browse

---
Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.