Round every coordinate to N decimal places to shrink a GeoJSON file's size — e.g. "my GeoJSON file is too big", "reduce coordinate precision", "strip excess decimal places". 6 decimals ≈ 11cm precision, plenty for most mapping use cases.
| Name | Required | Description | Default |
|---|---|---|---|
| geojson | Yes | ||
| decimals | No |
| 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 full burden. It discloses the lossy rounding behavior and the accuracy trade-off (6 decimals ≈ 11cm). While it doesn't state reversibility or file-size impact magnitude, the description is transparent about the core operation.
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 with no filler; the first clause front-loads the action and target, and the example intents and accuracy note earn their place.
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?
The tool is simple, has an output schema (so return format is covered), and the description provides purpose, usage guidance, parameter semantics, and a practical precision recommendation. It is complete for its complexity.
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 coverage is 0%, so the description must compensate. It maps 'N decimal places' to the decimals parameter and clarifies geojson as the input file. The precision guidance ('6 decimals ≈ 11cm') adds meaning beyond the schema's bare property names and types.
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 rounds every coordinate to N decimal places to shrink GeoJSON file size, distinguishing it from siblings like simplify_geometry (which simplifies shapes) and convert_coordinates (which changes coordinate systems).
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?
It provides explicit example user intents ('my GeoJSON file is too big', 'reduce coordinate precision') and notes that 6 decimals is sufficient for most mapping use cases. However, it doesn't explicitly contrast with alternative size-reduction tools like simplify_geometry, so exclusions are absent.
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.