Scan an ArcGIS FeatureServer layer for common problems: missing ObjectID, disabled query, stale data, all-null fields, features without geometry.
Sample-based (first 200 records). Returns JSON: {layer_name,
issues:[{severity, code, message}], issue_count, sample_size, note}.
| Name | Required | Description | Default |
|---|---|---|---|
| token | No | ||
| service_url | 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?
The description discloses key behavioral traits: it is sample-based (first 200 records), returns a JSON structure with layer_name, issues array, issue_count, sample_size, and note. This provides clear expectations beyond the missing annotations. No contradictions.
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, front-loading the main purpose and listing issues. It could be slightly more organized (e.g., separating parameter notes), but it is efficient with minimal waste.
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 an output schema (mentioned) and moderate complexity, the description covers the main scanning purpose, sample behavior, and return format. However, it omits parameter explanation, which is a gap for a tool with only two parameters.
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%, and the description does not explain the parameters 'service_url' or 'token'. Since the description adds no meaning beyond the schema, it fails to compensate for the lack of parameter documentation.
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 purpose: scanning an ArcGIS FeatureServer layer for common problems, listing specific issues (missing ObjectID, disabled query, stale data, etc.). This distinguishes it from sibling tools like audit_service or full_service_audit, which are more comprehensive or focused on other aspects.
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 implies usage for quick checks on layer health, but does not explicitly state when to use this tool versus alternatives like audit_service or full_service_audit. No when-not or exclusion criteria are given.
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.