OpenStreetMap MCP Server

explore_area

Analyze neighborhoods by generating detailed profiles of amenities and geographic features within a specified radius around coordinates for location-based research and comparisons.

Instructions

Generate a comprehensive profile of an area including all amenities and features.

This powerful analysis tool creates a detailed overview of a neighborhood or area by identifying and categorizing all geographic features, amenities, and points of interest. Results are organized by category for easy analysis. Excellent for neighborhood research, area comparisons, and location-based decision making.

Args: latitude: Center point latitude (decimal degrees) longitude: Center point longitude (decimal degrees) radius: Search radius in meters (defaults to 500m)

Returns: In-depth area profile including: - Address and location context - Total feature count - Features organized by category and subcategory - Each feature includes name, coordinates, and detailed metadata

Input Schema

TableJSON Schema

Name	Required	Description	Default
`latitude`	Yes
`longitude`	Yes
`radius`	No

Implementation Reference

src/osm_mcp_server/server.py:615-731 (handler)

Implementation of the explore_area tool handler. Queries OpenStreetMap Overpass API for multiple feature categories within a bounding box derived from center coordinates and radius. Groups results by subcategories and returns a comprehensive area profile including address information.

async def explore_area(
    latitude: float,
    longitude: float,
    ctx: Context,
    radius: float = 500
) -> Dict[str, Any]:
    """
    Generate a comprehensive profile of an area including all amenities and features.
    
    This powerful analysis tool creates a detailed overview of a neighborhood or area by
    identifying and categorizing all geographic features, amenities, and points of interest.
    Results are organized by category for easy analysis. Excellent for neighborhood research,
    area comparisons, and location-based decision making.
    
    Args:
        latitude: Center point latitude (decimal degrees)
        longitude: Center point longitude (decimal degrees)
        radius: Search radius in meters (defaults to 500m)
        
    Returns:
        In-depth area profile including:
        - Address and location context
        - Total feature count
        - Features organized by category and subcategory
        - Each feature includes name, coordinates, and detailed metadata
    """
    osm_client = ctx.request_context.lifespan_context.osm_client
    
    # Categories to search for
    categories = [
        "amenity", "shop", "tourism", "leisure", 
        "natural", "historic", "public_transport"
    ]
    
    results = {}
    for i, category in enumerate(categories):
        await ctx.report_progress(i, len(categories))
        ctx.info(f"Exploring {category} features...")
        
        try:
            # Convert radius to bounding box
            lat_delta = radius / 111000
            lon_delta = radius / (111000 * math.cos(math.radians(latitude)))
            
            bbox = (
                longitude - lon_delta,
                latitude - lat_delta,
                longitude + lon_delta,
                latitude + lat_delta
            )
            
            features = await osm_client.search_features_by_category(bbox, category)
            
            # Group by subcategory
            subcategories = {}
            for feature in features:
                tags = feature.get("tags", {})
                subcategory = tags.get(category)
                
                if subcategory:
                    if subcategory not in subcategories:
                        subcategories[subcategory] = []
                    
                    # Get coordinates based on feature type
                    coords = {}
                    if feature.get("type") == "node":
                        coords = {
                            "latitude": feature.get("lat"),
                            "longitude": feature.get("lon")
                        }
                    elif "center" in feature:
                        coords = {
                            "latitude": feature.get("center", {}).get("lat"),
                            "longitude": feature.get("center", {}).get("lon")
                        }
                    
                    subcategories[subcategory].append({
                        "id": feature.get("id"),
                        "name": tags.get("name", "Unnamed"),
                        "coordinates": coords,
                        "type": feature.get("type"),
                        "tags": tags
                    })
            
            results[category] = subcategories
            
        except Exception as e:
            ctx.warning(f"Error fetching {category} features: {str(e)}")
            results[category] = {}
    
    # Get address information for the center point
    try:
        address_info = await osm_client.reverse_geocode(latitude, longitude)
    except Exception:
        address_info = {"error": "Could not retrieve address information"}
    
    # Report completion
    await ctx.report_progress(len(categories), len(categories))
    
    # Count total features
    total_features = sum(
        len(places)
        for category_data in results.values()
        for places in category_data.values()
    )
    
    return {
        "query": {
            "latitude": latitude,
            "longitude": longitude,
            "radius": radius
        },
        "address": address_info,
        "categories": results,
        "total_features": total_features,
        "timestamp": datetime.now().isoformat()
    }

src/osm_mcp_server/server.py:144-177 (helper)

OSMClient helper method that performs the core Overpass API query to fetch OSM features (nodes, ways, relations) filtered by a specific category tag within a bounding box. Called repeatedly by explore_area for each category.

async def search_features_by_category(self, 
                                     bbox: Tuple[float, float, float, float],
                                     category: str,
                                     subcategories: List[str] = None) -> List[Dict]:
    """Search for OSM features by category and subcategories"""
    if not self.session:
        raise RuntimeError("OSM client not connected")
    
    overpass_url = "https://overpass-api.de/api/interpreter"
    
    # Build query for specified category and subcategories
    if subcategories:
        subcategory_filters = " or ".join([f'"{category}"="{sub}"' for sub in subcategories])
        query_filter = f'({subcategory_filters})'
    else:
        query_filter = f'"{category}"'
    
    query = f"""
    [out:json];
    (
      node[{query_filter}]({bbox[1]},{bbox[0]},{bbox[3]},{bbox[2]});
      way[{query_filter}]({bbox[1]},{bbox[0]},{bbox[3]},{bbox[2]});
      relation[{query_filter}]({bbox[1]},{bbox[0]},{bbox[3]},{bbox[2]});
    );
    out body;
    """
    
    async with self.session.post(overpass_url, data={"data": query}) as response:
        if response.status == 200:
            data = await response.json()
            return data.get("elements", [])
        else:
            raise Exception(f"Failed to search features by category: {response.status}")

src/osm_mcp_server/server.py:45-64 (helper)

OSMClient helper method used by explore_area to fetch address information for the center point of the exploration area via Nominatim reverse geocoding.

async def reverse_geocode(self, lat: float, lon: float) -> Dict:
    """Reverse geocode coordinates to address"""
    if not self.session:
        raise RuntimeError("OSM client not connected")
    
    nominatim_url = "https://nominatim.openstreetmap.org/reverse"
    async with self.session.get(
        nominatim_url,
        params={
            "lat": lat,
            "lon": lon,
            "format": "json"
        },
        headers={"User-Agent": "OSM-MCP-Server/1.0"}
    ) as response:
        if response.status == 200:
            return await response.json()
        else:
            raise Exception(f"Failed to reverse geocode ({lat}, {lon}): {response.status}")

src/osm_mcp_server/server.py:621-640 (schema)

Tool schema documentation including description, input parameters (latitude, longitude, radius), and output structure description.

"""
Generate a comprehensive profile of an area including all amenities and features.

This powerful analysis tool creates a detailed overview of a neighborhood or area by
identifying and categorizing all geographic features, amenities, and points of interest.
Results are organized by category for easy analysis. Excellent for neighborhood research,
area comparisons, and location-based decision making.

Args:
    latitude: Center point latitude (decimal degrees)
    longitude: Center point longitude (decimal degrees)
    radius: Search radius in meters (defaults to 500m)
    
Returns:
    In-depth area profile including:
    - Address and location context
    - Total feature count
    - Features organized by category and subcategory
    - Each feature includes name, coordinates, and detailed metadata
"""

Schema Changelog

Changes observed during successful MCP inspections.

First observedAug 15, 2025

TDQS

A4.3/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description must carry the burden of behavioral disclosure. It describes the tool as an 'analysis tool' and details the return structure, implying a read-only, non-destructive operation. However, it does not explicitly state that the operation is safe or disclose potential limitations such as result volume, rate limits, or error behavior. This is a moderate gap given the lack of annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and front-loaded: a one-sentence summary immediately conveys purpose, followed by a brief elaboration, then clearly formatted Args and Returns sections. Every sentence contributes useful information without unnecessary fluff, making it appropriately sized for a tool of this complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description is largely complete: it explains all three parameters, provides a detailed Returns section (compensating for the missing output schema), and gives usage context. It lacks only explicit limitations or exclusions, such as maximum radius or performance caveats, but overall it gives enough for an agent to understand what the tool does and what to expect.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description provides a dedicated Args section that adds meaning beyond the schema: latitude and longitude are specified as 'Center point latitude (decimal degrees)' and the radius is 'in meters (defaults to 500m)'. Since the schema itself provides no descriptions, this fully compensates and clarifies units and semantics for all parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with 'Generate a comprehensive profile of an area including all amenities and features,' which clearly states a specific action and resource. It further specifies that it identifies and categorizes geographic features, amenities, and points of interest, distinguishing it from narrower siblings like find_nearby_places or search_category.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states use cases: 'Excellent for neighborhood research, area comparisons, and location-based decision making.' This gives clear context on when to use the tool. However, it does not explicitly mention when not to use it or directly reference alternative tools, so it stops short of a full when/where-not comparison.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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