Convert Google Earth / Google My Maps KML (2.0–2.2) to GeoJSON — the common request when someone exported a map from Google Earth or Google My Maps and needs it in a format other GIS tools/APIs accept (e.g. "convert my Google Earth file to GeoJSON", "KML to GeoJSON").
Returns the GeoJSON FeatureCollection as a JSON string.
| 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?
No annotations are present, so the description carries the full burden. It states the output is a 'GeoJSON FeatureCollection as a JSON string' and specifies the supported KML versions, offering basic transparency. However, it does not disclose potential limitations (e.g., handling of unsupported KML features, coordinate system behavior) or 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is reasonably concise and front-loaded with the core action. The example queries in parentheses add useful context for matching user intent without being excessively verbose. The structure is clear, though the second paragraph could be tighter.
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?
Given the simplicity (one parameter) and the presence of an output schema, the description covers the main purpose, inputs (implicitly), and output format. However, the lack of explicit parameter clarification and any note on edge cases or errors leaves some gaps, making it adequate but not comprehensive.
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 one parameter 'kml' with no description at all (coverage 0%), so the description must compensate. It only implies through context that the parameter should be the KML content, but it never explicitly states that 'kml' expects the raw KML XML string, nor does it clarify any formatting requirements.
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 a specific verb ('Convert') and resource ('Google Earth / Google My Maps KML (2.0–2.2)') with a specific output ('GeoJSON'). It distinguishes this from sibling tools like kml_to_shapefile or gpx_to_geojson by naming the exact source and target formats.
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 clear context on when to use the tool: when someone has exported a map from Google Earth or Google My Maps and needs a format other GIS tools accept. It does not explicitly name alternative tools or exclusions, but the context is sufficient to infer appropriate use cases.
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.