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append_gpd

Read two shapefiles and combine them vertically into one output shapefile for integrated geospatial data.

Instructions

Reads two shapefiles directly, concatenates them vertically.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
shapefile1_pathYes
shapefile2_pathYes
output_pathYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The append_gpd function is the core handler that reads two shapefiles, ensures matching CRS, concatenates them vertically using pd.concat, and saves the result. It is registered as an MCP tool via @gis_mcp.tool() decorator.
    @gis_mcp.tool()
    def append_gpd(shapefile1_path: str, shapefile2_path: str, output_path: str) -> Dict[str, Any]:
        """ Reads two shapefiles directly, concatenates them vertically."""
        try:
            # Configure a basic logger for demonstration
            logging.basicConfig(level=logging.INFO)
            logger = logging.getLogger(__name__)
    
            # Step 1: Read the two shapefiles into GeoDataFrames.
            logger.info(f"Reading {shapefile1_path}...")
            gdf1 = gpd.read_file(shapefile1_path)
            
            logger.info(f"Reading {shapefile2_path}...")
            gdf2 = gpd.read_file(shapefile2_path)
    
            # Step 2: Ensure the Coordinate Reference Systems (CRS) match.
            if gdf1.crs != gdf2.crs:
                logger.warning(
                    f"CRS mismatch: GDF1 has '{gdf1.crs}' and GDF2 has '{gdf2.crs}'. "
                    "Reprojecting GDF2."
                )
                gdf2 = gdf2.to_crs(gdf1.crs)
    
            # Step 3: Concatenate the two GeoDataFrames.
            combined_gdf = pd.concat([gdf1, gdf2], ignore_index=True)
    
            # Step 4: Save the combined GeoDataFrame to a new shapefile.
            output_path_resolved = resolve_path(output_path, relative_to_storage=True)
            output_path_resolved.parent.mkdir(parents=True, exist_ok=True)
            logger.info(f"Saving combined shapefile to {output_path_resolved}...")
            combined_gdf.to_file(str(output_path_resolved), driver='ESRI Shapefile')
    
            return {
                "status": "success",
                "message": f"Shapefiles concatenated successfully into '{output_path_resolved}'.",
                "info": {
                    "output_path": str(output_path_resolved),
                    "num_features": len(combined_gdf),
                    "crs": str(combined_gdf.crs),
                    "columns": list(combined_gdf.columns)
                }
            }
        
        except Exception as e:
            logger.error(f"Error processing shapefiles: {str(e)}")
            raise ValueError(f"Failed to process shapefiles: {str(e)}")
  • The string "append_gpd" is listed as an available operation under the gis://geopandas/joins resource, serving as a registration/reference entry point for this tool.
    @gis_mcp.resource("gis://geopandas/joins")
    def get_geopandas_joins() -> Dict[str, List[str]]:
        """List available GeoPandas join operations."""
        return {
            "operations": [
                "append_gpd",
                "merge_gpd",
                "sjoin_gpd",
                "sjoin_nearest_gpd",
                "point_in_polygon"
            ]
        }
  • Test for append_gpd: creates two GeoDataFrames, calls the tool via client.call_tool('append_gpd', ...), and asserts success.
    async def test_append_gpd(self, sample_geodataframe, temp_dir):
        """Test appending GeoDataFrames."""
        _, file1 = sample_geodataframe
        # Create second file
        gdf2 = gpd.GeoDataFrame(
            {'id': [4, 5], 'name': ['D', 'E'], 'value': [40, 50]},
            geometry=[Point(3, 3), Point(4, 4)],
            crs='EPSG:4326'
        )
        file2 = os.path.join(temp_dir, "test2.shp")
        gdf2.to_file(file2)
        
        output_path = os.path.join(temp_dir, "appended.shp")
        async with Client(gis_mcp) as client:
            result = await client.call_tool("append_gpd", {
                "shapefile1_path": file1,
                "shapefile2_path": file2,
                "output_path": output_path
            })
            result_data = get_result_data(result)
            assert result_data["status"] == "success"
            assert os.path.exists(output_path)
  • src/gis_mcp/mcp.py:1-6 (registration)
    The FastMCP instance 'gis_mcp' is created here. The @gis_mcp.tool() decorator on append_gpd uses this instance to register the tool.
    # MCP imports using the new SDK patterns
    from fastmcp import FastMCP
    
    
    gis_mcp = FastMCP("GIS MCP")
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It only states 'reads two shapefiles directly' but does not disclose if inputs are modified, what happens with mismatched schemas, or any side effects.

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

Conciseness3/5

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

The description is a single sentence with no wasted words, but it is too brief. It could be expanded without losing conciseness to add valuable detail.

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

Completeness2/5

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

With 0% schema description coverage and no annotations, the description is insufficient. It does not cover parameter purposes, return behavior, or how the tool handles edge cases like differing geometries.

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

Parameters2/5

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

Schema description coverage is 0%, and the description does not explain any of the three parameters. The tool name and description hint at shapefile paths and output, but no explicit mapping or format details are given.

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

Purpose4/5

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

Description clearly states it concatenates two shapefiles vertically. It uses specific verbs and resources, but does not distinguish from sibling tool merge_gpd, which may perform a similar operation.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives like merge_gpd, dissolve_gpd, or sjoin_gpd. The description implies concatenation but does not describe context or prerequisites.

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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