Divide space into regions closest to each input point (Voronoi diagram) — e.g. "find the service area for each location", "which points are closest to which store", "Voronoi/Thiessen polygons". Non-point features use their centroid. Needs at least 2 points. Returns JSON {ok, cell_count, geojson}.
| 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?
With no annotations provided, the description carries the burden of behavioral disclosure. It explains how non-point features are treated ('Non-point features use their centroid'), the minimum input requirement, and the return structure ('Returns JSON {ok, cell_count, geojson}'). This goes beyond the schema, but lacks details on error conditions or coordinate system handling.
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 concise, with three sentences that front-load the core definition, then add examples, constraints, and return format. Every sentence contributes value without redundancy.
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 tool has only one parameter and a documented output schema, the description provides ample context for an agent: purpose, examples, edge-case handling, minimum input, and return shape. It covers the essential aspects without overloading.
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 description coverage is 0%—the only parameter 'geojson' is just typed as 'string' with no explanation. The description adds that at least 2 input points are needed and non-point features use centroids, but it does not specify the expected GeoJSON structure (e.g., FeatureCollection vs single feature). This is useful but still leaves some ambiguity.
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 tool's function: 'Divide space into regions closest to each input point (Voronoi diagram)' with concrete examples like 'service area for each location'. This specific verb+resource framing distinguishes it from sibling tools such as buffer_geojson or convex_hull_geojson.
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 for when to use the tool via examples and prerequisites (e.g., 'Needs at least 2 points'). However, it does not explicitly mention alternatives or when not to use it, so it misses the full 'when-not' guidance.
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.