Get the ground elevation (metres above sea level) at a coordinate — e.g. "what's the elevation here", "is this point in a flood-risk lowland", "elevation at this address". Via Open-Meteo (Copernicus DEM GLO-90, no key, free, ~90m resolution).
Returns JSON: {ok, lon, lat, elevation_m, source}.
| Name | Required | Description | Default |
|---|---|---|---|
| lat | Yes | ||
| lon | 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 discloses the data source (Open-Meteo Copernicus DEM), no API key needed, free usage, ~90m resolution, and the return JSON structure. Lacks error handling or coordinate order details, but provides substantial context.
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?
Three sentences covering purpose, data source, and return format. The return format sentence is redundant given the output schema, but it is brief and adds emphasis. Overall efficient with no fluff.
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 simple 2-param read-only tool with an output schema, the description covers purpose, data source, resolution, no-key access, and return shape. Missing edge cases are not critical for selection and invocation.
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?
Schema description coverage is 0%, so the description must compensate. It refers to 'a coordinate' and output includes lon/lat, but does not explicitly state coordinate format (decimal degrees) or that addresses require prior geocoding. The example 'elevation at this address' could mislead an agent into passing an address string.
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?
Clearly states 'Get the ground elevation (metres above sea level) at a coordinate' with specific unit and resource. Examples like 'what's the elevation here' help distinguish it from sibling assessment tools that perform broader analyses.
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?
Provides example intents (flood-risk lowland, address elevation) that imply when to use. Does not explicitly contrast with sibling tools like assess_property_hazard, but the context is clear for a direct elevation lookup.
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.