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". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds specific output details (fields with name+type, geometry type, record count, capabilities) beyond the annotations, which already declare read-only and idempotent behavior. It does not mention error handling or rate limits, but given the strong annotation coverage, this is still solid.
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 a single, well-structured sentence with upfront verb placement and no redundant words. It packs the return contents into a compact list, earning 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 simple read-only tool with one parameter and no output schema, the description adequately covers the return values and the target service type. It lacks caveats about invalid URLs or Map Server variants, but these are minor gaps given the simplicity.
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 documents the 'url' parameter with a clear description and example (100% coverage). The tool description merely repeats 'by url' without adding new semantic detail, so it sits at the baseline.
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 clearly identifies the resource ('an ArcGIS Feature/Map Service layer's schema') with a detailed list of what it returns (fields, geometry type, record count, capabilities). This distinguishes it from sibling tools like query_layer, which would retrieve data rather than schema.
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 clearly states what the tool does but does not explicitly mention when to use it over alternatives or provide exclusions. While the purpose implies usage for schema retrieval, there is no direct comparison to siblings such as query_layer or search_within.
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.
All 33 tools have clearly distinct purposes, even those that seem related like ask_pipeworx, ask_pipeworx_grounded, and deep_research are well-differentiated by use case and behavior. Memory tools (remember/recall/forget) and subscription tools (subscribe/unsubscribe/list_subscriptions/recent_alerts) are similarly distinct.
All tools use snake_case, but naming patterns are mixed: some are single verbs (forget, recall), some verb_noun (query_layer, search_datasets), some noun_noun (entity_profile, layer_info), and some longer phrases (polymarket_kalshi_spread, scan_competitor_ai_presence). While readable, the inconsistency makes the set feel less coherent.
With 33 tools, the surface is overly broad for a server named after a specific ArcGIS dataset. Many tools are unrelated to the core purpose (e.g., polymarket tools, npm scanning, AI visibility), making the count feel bloated and unfocused.
For the stated ArcGIS Branson focus, only 3 tools (search_datasets, query_layer, layer_info) are relevant, offering only read access. The rest are a miscellaneous collection from the Pipeworx ecosystem and other domains, leaving obvious gaps in GIS functionality and no write or analysis capabilities.