layer_infoGet an ArcGIS Feature/Map Service layer's schema by url: fields (name + type), geometry type, total record count, and capabilities.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Feature/Map Service layer url, e.g. ".../FeatureServer/0". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "url": "https://services.arcgis.com/glasgowcc/arcgis/rest/services/Parcels/FeatureServer/0"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as read-only, open-world, idempotent, and non-destructive. The description adds value by detailing the response contents (fields, geometry type, record count, capabilities), giving agents a clear expectation of what the tool returns. No behavioral traits are hidden, and there is no contradiction with annotations.
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 one compact sentence that front-loads the action and resource, then packs the key output details into a clean list. Every word earns its place; no filler or 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?
For a simple single-parameter read tool, the description is sufficiently complete: it states the tool's purpose, lists the returned information, and benefits from strong annotations and schema coverage. It doesn't mention potential errors (e.g., invalid URL) or rate limits, but these are not critical for a read-only schema fetcher.
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 input schema already provides 100% coverage for the single 'url' parameter, including an example and format hint. The description simply repeats 'by url' without adding extra detail about URL construction, validation, or edge cases. Baseline score of 3 is appropriate because the schema handles the burden.
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 uses a specific verb 'Get' and identifies the resource as 'an ArcGIS Feature/Map Service layer's schema'. It enumerates the exact outputs (fields, geometry type, record count, capabilities), making it clear this is a metadata inspection tool distinct from data querying siblings like query_layer.
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 provides no guidance on when to use this tool versus alternatives. While it implies using it when you need schema information, it doesn't mention that query_layer should be used for actual data retrieval, nor does it note any prerequisites or exclusions. Sibling tools like query_layer and search_within exist, but the description doesn't help choose between them.
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.
Several tools have unclear boundaries: ask_pipeworx, ask_pipeworx_beta (currently functionally identical), ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all route to the same underlying catalog with only subtle differences. The Polymarket cluster (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) also substantially overlaps in purpose, and the two unrelated domains (GIS vs. data/betting) make it worse.
Names are readable and form some predictable clusters (polymarket_* prefix, ask_pipeworx_* suffixes, subscribe/unsubscribe/list_subscriptions), but conventions are mixed: bare verbs (remember, forget, recall), noun_noun (layer_info, entity_profile, pipeworx_feedback), verb_noun (query_layer, search_datasets), and adjective_noun (deep_research, recent_alerts). No single pattern dominates, though nothing is chaotic.
34 tools is well above the 25+ threshold for a heavy surface, and the count is not justified by the server's stated identity: only 3 of 34 tools (search_datasets, layer_info, query_layer) relate to ArcGIS Glasgow. The remaining 31 tools belong to several unrelated domains (Pipeworx data querying, Polymarket betting, AI visibility, memory, npm scanning), making the effective scope far too broad.
For the server's named ArcGIS Glasgow domain, the surface is thin: search, schema inspection, and query are present, but there is no way to list all datasets, no spatial querying, and no write/update capability. Meanwhile the 31 non-GIS tools create a sprawling second server's worth of functionality, so the set as a whole has no coherent domain whose coverage can be judged complete.