Convert a CSV with latitude/longitude columns directly to GPX waypoints (2026-09-03) — e.g. "these coordinates to a GPX for my GPS". Column names are auto-detected (lat/latitude/y, lon/lng/longitude/x).
| Name | Required | Description | Default |
|---|---|---|---|
| csv | 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 must disclose behavior on its own. It reveals that output is limited to waypoints and that column names are auto-detected across several aliases. It stays silent on assumptions like header rows, delimiters, coordinate ordering, or failure behavior when required columns are missing.
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 short and front-loaded with the core action and output. The unexplained parenthetical date '(2026-09-03)' adds noise and prevents a perfect score.
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 single-parameter converter with an output schema available, the description covers the main input content, accepted column names, and output type. It omits explicit routing among sibling converters and a few input-format assumptions, but nothing critical for the common case is missing.
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 is the only source of meaning for the 'csv' parameter. It adds concrete semantics by enumerating accepted column aliases and stating auto-detection. It does not clarify whether 'csv' is raw text, a file path, or a URL, nor does it mention delimiter or header expectations.
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 states the action ('Convert'), the input (a CSV with latitude/longitude columns), and the result (GPX waypoints). The word 'directly' differentiates it from multi-step conversion flows involving sibling tools like csv_to_geojson followed by geojson_to_gpx.
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 example 'these coordinates to a GPX for my GPS' gives a clear user-intent trigger, and 'directly' signals that no intermediate conversion is needed. However, it does not explicitly name sibling alternatives or state when not to use this tool.
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.