list_subscriptionsList the caller's active subscriptions. Returns id, type, params, created_at, last_fired_at, fire_count for each. Use this to review what you're monitoring before adding more or to find an id to cancel.
| Name | Required | Description | Default |
|---|---|---|---|
| include_inactive | No | Include cancelled subscriptions in the response (default false). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, non-destructive, idempotent behavior. The description adds value by disclosing the return field list, caller scoping ('caller's'), and the active-only default, all beyond what annotations convey.
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 concise sentences. The first states the action and return fields; the second provides usage guidance. No filler or redundant content.
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 list tool with one optional parameter, the description covers scope, return fields, and use cases. Annotations handle safety. No important gaps apparent.
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 coverage is 100% for the single optional parameter include_inactive, and the schema description is clear. The tool description does not add parameter-specific semantics, but the baseline of 3 is appropriate given the schema does the heavy lifting.
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 lists the caller's active subscriptions, uses a specific verb ('List') and resource, and enumerates the returned fields. It distinguishes itself from subscription-related siblings by framing use cases (reviewing before adding more, finding an id to cancel).
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?
Provides explicit when-to-use context: 'review what you're monitoring before adding more' and 'find an id to cancel.' This implies alternatives (subscribe/unsubscribe) without naming them explicitly, so it stops short of a 5.
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 overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all route questions to similar data sources, and ask_pipeworx_beta is explicitly identical to ask_pipeworx right now. The many polymarket tools also blur together despite detailed descriptions.
Most names are snake_case, but there is no consistent verb_noun pattern: ask_pipeworx, bet_research, entity_profile, layer_info, pipeworx_trending, recent_changes, and validate_claim follow different stylistic conventions. The pattern is readable but feels like several naming vocabularies were merged.
34 tools is well over the comfortable range and most of them are unrelated to the apparent ArcGIS Johnson City purpose. Only search_datasets, layer_info, and query_layer actually serve GIS needs; the remaining 31 tools form a sprawling Pipeworx meta-platform bolted onto the same server.
The ArcGIS portion covers discover-schema-query reasonably well for read-only open data, and the Pipeworx side has broad coverage with subscriptions, memory, feedback, and research workflows. However, the surface is defined by two unrelated domains, making it hard to judge true completeness for any one stated purpose.