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 declare read-only and non-destructive. Description adds the response shape (id, type, params, created_at, last_fired_at, fire_count) and clarifies it returns active subscriptions by default, giving context beyond 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?
Two crisp sentences front-load the purpose and return fields, then give actionable usage guidance. No filler.
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 operation with one optional parameter, the description covers purpose, return fields, and usage. No output schema exists, but the field list compensates.
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?
Only parameter include_inactive is fully described in the schema, including default. Description reinforces 'active subscriptions' but adds no new parameter detail beyond schema's 100% coverage.
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?
Clearly states what it does: lists active subscriptions. Provides return fields and distinguishes from subscribe/unsubscribe by mentioning use cases for adding or cancelling.
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?
Explicitly advises when to use: to review current monitoring before adding more subscriptions or to get an id for cancellation. This gives direct context against sibling tools.
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.
Most tools have distinct purposes, but the ask/research family is crowded: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route to the same underlying catalog and require careful description reading to select correctly. Prediction-market tools also overlap in scope, though each has a reasonably distinct angle.
All names are snake_case and mostly readable, but conventions are mixed: some are verb_noun (query_layer, resolve_entity), some are noun_noun (entity_profile, pipeworx_feedback, polymarket_arbitrage), and some are adjective_noun or brand-prefixed phrases. No consistent verb/noun ordering pattern exists across the set.
34 tools is too many for the apparent focus, especially since the server is named 'Arcgis Phoenix' but only 3 of the tools are actually GIS tools. The bulk is a sprawling Pipeworx data/prediction-market ecosystem plus unrelated utilities, making the set feel over-stuffed and unfocused.
The Pipeworx data side is fairly complete for lookups, entity profiles, comparisons, validation, and subscriptions, but the ArcGIS Phoenix portion is only search/schema/query and lacks any analysis, geocoding, or editing capability. The presence of generate_llms_txt and scan_dependency highlights that the overall domain is undefined and therefore hard to call complete.