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 readOnlyHint, idempotentHint, and destructiveHint false, so no safety concerns. The description adds value by listing the returned fields and implying no side effects, which is consistent.
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, front-loaded with purpose and output, then usage guidance. No redundant information.
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 boolean parameter and no output schema, the description fully covers what the tool does, what it returns, and when to use it.
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%; the single parameter 'include_inactive' is fully described in the schema. The tool description does not add any additional meaning beyond what is already in 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 it lists active subscriptions and specifies returned fields. It implicitly distinguishes from siblings like subscribe/unsubscribe by mentioning finding ids to cancel, but does not explicitly differentiate.
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 states when to use: 'Use this to review what you're monitoring before adding more or to find an id to cancel,' guiding the agent to appropriate contexts and suggesting 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.
While many tools have distinct purposes, there is overlap between ask_pipeworx, ask_pipeworx_grounded, and deep_research, as well as multiple Polymarket and entity-related tools. Descriptions are detailed but could confuse agents on which tool to use for a specific query.
Most tool names use snake_case and many start with verbs, but there is inconsistency with noun-starting names like entity_profile, layer_info, and pipeworx_feedback. No strong verb_noun or other consistent pattern across the set.
With 33 tools, the count is relatively high but could be justified for a broad data platform. However, given the server name 'Arcgis Sanjose', the number seems excessive as most tools are unrelated to ArcGIS geospatial functions.
The tool set is severely incomplete for the implied ArcGIS San Jose domain, with only 3 tools (search_datasets, layer_info, query_layer) supporting that purpose. The rest are from the Pipeworx ecosystem, creating a mismatch between server name and actual functionality.