Compute area (m²/km²), length (m/km), vertex count, centroid and bbox of GeoJSON.
Metric measurements use an equal-area projection so area/length are real metres.
| Name | Required | Description | Default |
|---|---|---|---|
| geojson | 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 provided, so description carries full burden. Discloses use of equal-area projection for metric accuracy, which is key behavioral detail. Lacks notes on error handling or input validation, but tool is read-only and simple.
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?
Two sentences, front-loaded with main outputs, followed by projection detail. 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?
Tool has output schema, so return values are covered. Description covers main functionality and key nuance (equal-area projection). For a single-parameter tool, it is complete.
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?
Only one parameter 'geojson' with no schema description (0% coverage). Description mentions 'of GeoJSON' implying expects a GeoJSON string, adding some meaning but not specifying format requirements or examples.
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?
Description clearly states the tool computes area, length, vertex count, centroid, and bbox of GeoJSON, using specific verbs and resource. It distinguishes from sibling geometry tools (e.g., buffer, convex hull) by focusing on statistics.
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 context on when to use (accurate metric measurements via equal-area projection) but does not explicitly state when not to use or suggest alternatives.
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.