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://gis.loudoun.gov/arcgis/rest/services/Public/Parcels/FeatureServer/0"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already establish read-only, idempotent, non-destructive behavior. The description adds value by specifying the type of network operation (fetching from a URL) and what information will be returned (fields, geometry type, record count, capabilities), providing behavioral expectations beyond the annotations. No contradictions 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 a single, front-loaded sentence that efficiently packs all essential information: action, resource, and output components. There is no fluff or repetition, and it is immediately understandable.
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, one-parameter, read-only tool with no output schema, the description fully covers what the agent needs to know: the input (URL) and the returned information (schema fields, geometry type, record count, capabilities). No critical details are missing for effective tool selection and invocation.
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 has 100% coverage for the single 'url' parameter, so the baseline is 3. The description adds minimal extra meaning, merely reinforcing that the URL is a layer endpoint, which the schema already states. It does not introduce new details about parameter format or constraints.
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 ('ArcGIS Feature/Map Service layer's schema') along with the exact components (fields, geometry type, record count, capabilities). This clearly distinguishes it from sibling tools like query_layer or search_within, which likely operate on 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 implies the tool is for retrieving layer metadata by URL, giving a clear use case. However, it does not explicitly mention alternatives or when not to use it, though the sibling list suggests tasks like querying features are handled elsewhere. This earns a 4 for clear context without explicit exclusions.
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 tools have distinct purposes, but there is overlap among data query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research) and numerous Polymarket bet tools, which could cause confusion. Tool descriptions are detailed and help differentiate, but the diversity of domains requires careful reading.
Tool names follow inconsistent patterns: some are snake_case verb_noun (query_layer, search_datasets), others are noun_verb (bet_research) or compound names (pipeworx_feedback, polymarket_arbitrage). There is no uniform convention, making it harder for agents to predict tool names.
With 33 tools, the server feels overloaded for its apparent ArcGIS focus. Most tools are unrelated to ArcGIS (Polymarket, Pipeworx data, memory, subscriptions), suggesting a lack of scope. The count is high without a clear unifying purpose.
The tool surface is incomplete for any single domain. ArcGIS coverage is minimal (only query and schema), Pipeworx data tools are abundant but without a clear workflow, and Polymarket betting lacks order placement. The server tries to cover too many areas resulting in shallow coverage.