Convert a Google Earth/My Maps KML file directly to an Esri Shapefile ZIP — e.g. "Google Earth file to shapefile for ArcGIS", "KML to SHP". Returns JSON: {shapefile_zip_base64, warnings}.
| Name | Required | Description | Default |
|---|---|---|---|
| kml | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the conversion behavior, the output format (JSON with shapefile_zip_base64 and warnings), and implies a safe, non-destructive operation. It could mention limitations or size constraints, but the return structure and warnings add useful behavioral transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is only two sentences long, front-loads the core purpose in the first words, and every phrase adds value—including the examples and return structure. No redundancy or unnecessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter conversion tool, the description covers the essential context: what it converts, the output format, and the JSON structure. Given the output schema exists, it doesn't need to enumerate return fields in depth, and the description is complete enough for an agent to invoke it correctly. Minor gaps include lack of error handling or edge cases, but these are not critical for a simple converter.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage for the 'kml' parameter, and the description only indirectly indicates it is the KML file content. It doesn't explicitly clarify whether it's the raw XML string, a file path, or a URL. The parameter name is intuitive, but the description does not fully compensate for the schema's lack of detail.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the conversion from KML to Shapefile ZIP with a specific verb ('Convert') and resources ('Google Earth/My Maps KML file' to 'Esri Shapefile ZIP'). It also provides search phrase examples that help identify the tool's exact purpose, distinguishing it from sibling converters like kml_to_geojson and geojson_to_shapefile.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives implicit usage context by showing example queries ('Google Earth file to shapefile for ArcGIS', 'KML to SHP') that indicate when this tool is appropriate. However, it does not explicitly mention alternatives or when not to use it, which would warrant a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Many assess_* composite tools (datacenter, ev_charging, renewable, telecom, due_diligence) share the same building blocks of grid proximity, land cover, and terrain, making their boundaries fuzzy. assess_property_hazard_x402 also duplicates assess_property_hazard with only a payment-method difference, and bundle_* tools intentionally overlap with the free primitives they replace.
There is a mix of verb_noun tools (query_features, geocode_address), noun-first geometry tools (centroids_geojson, envelope_geojson), and inconsistent _geojson suffix usage (buffer_geojson, fix_geometry, geometry_stats). The assess_* and bundle_* prefixes offer some grouping, but no single naming pattern is followed across the set.
With 85 tools, the surface is extreme for an MCP server and far exceeds the typical well-scoped 3-15 range. Many tools are convenience bundles or variants that could be consolidated, making the count a significant usability burden.
The toolkit covers a broad range of GIS tasks: format conversions, GeoJSON analysis, FeatureServer query/inspection, geocoding, site assessment, and sharing. Minor gaps exist (e.g., no Excel-to-GeoJSON, no FeatureServer update/delete), but most missing functionality can be worked around by chaining existing tools.