denver_layersList the layers of a Denver ArcGIS service (for discovery). Pass a known short name (crime) or a full ArcGIS service path (e.g. "ODC_CRIME_OFFENSES_P/FeatureServer"). Omit service to list the known Denver services. Returns layer id + name to use with denver_query.
| Name | Required | Description | Default |
|---|---|---|---|
| service | No | Short name (crime) or full ArcGIS service path. Omit to list known services. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the description's addition of 'for discovery' and the return of layer id+name adds moderate behavioral context. No contradictions, but no extra details on auth or rate limits.
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?
Two sentences, no extraneous words. Front-loaded with the core purpose and then details on usage. Every sentence adds value.
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 discovery tool with one optional parameter and no output schema, the description covers input options, output purpose, and relationship to denver_query. No gaps.
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 100%, and the description largely mirrors the schema's parameter description (e.g., short name, full path, omit to list services). The examples in the schema add value, but the description does not significantly augment the schema.
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 verb 'List' and the resource 'layers of a Denver ArcGIS service (for discovery)'. It specifies how to use short names or full paths and what happens when 'service' is omitted, distinguishing it from sibling tools like denver_query and denver_recent.
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 explains when to omit the service parameter to list known services, and how to pass short names or full paths. It notes the output is for use with denver_query, providing context for tool chaining. However, it does not explicitly state when not to use this tool or mention alternatives.
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.
Tools are richly described with clear use-cases, but several clusters overlap: ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded/deep_research are all question-routing tools with similar names, and the five polymarket_* tools cover adjacent prediction-market analysis. An agent could easily pick the wrong one without reading the full descriptions.
All names are snake_case and mostly follow a verb_noun or domain_noun pattern (compare_entities, resolve_entity, denver_query, polymarket_edges, pipeworx_trending). There are minor deviations like single verbs (remember, forget), adjective-first names (recent_alerts, deep_research), and domain-prefixed groups, but the overall style is predictable and readable.
34 tools is a large surface for one server, pushing beyond the 25+ threshold where agents struggle to choose. While the platform is genuinely multi-domain (data lookup, prediction markets, Denver open data, memory, subscriptions, npm checks, llms.txt generation), several niche clusters like the 5-tool Polymarket suite and 3-tool memory trio inflate the count.
The server covers its domain well: general querying, grounded verification, deep research, entity resolution, company profiling, change feeds, subscription lifecycle (subscribe/list/unsubscribe/alerts), memory lifecycle (remember/recall/forget), and discovery (discover_tools, suggest_questions). Minor gaps exist—such as no direct browsing of all Pipeworx sources—but agents can work around them.