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). |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. The description adds valuable context by listing return fields and mentioning that it's scoped to the caller. It doesn't discuss the include_inactive parameter behavior, but the schema covers that. This goes beyond annotations without contradicting them.
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 two sentences, front-loaded with the purpose, and includes return fields and usage guidance without any fluff. Every sentence earns its place.
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 tool with one optional parameter and no output schema, the description fully covers purpose, usage, and return fields. It is complete enough for an agent to select and invoke correctly.
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% and the single parameter include_inactive is fully described in the schema. The description's mention of 'active subscriptions' implies the default behavior but adds no additional semantics beyond 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 tool lists the caller's active subscriptions, with specific fields returned, distinguishing it from mutation siblings like subscribe and unsubscribe.
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?
Explicit usage guidance is given: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This clearly indicates when to use it and implies the alternatives (subscribe/unsubscribe).
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.
The descriptions are extraordinarily detailed and genuinely differentiate most tools, but the set contains near-clones (ask_pipeworx, ask_pipeworx_beta which is explicitly 'identical' to it, and ask_pipeworx_grounded) plus five polymarket tools whose boundaries (arbitrage vs edges vs edge_tracker vs fill_risk vs kalshi_spread) overlap enough to cause misselection. An agent navigating this surface must read full descriptions to choose correctly, which defeats quick tool selection.
Most tools follow a snake_case verb_noun pattern (ask_pipeworx, compare_entities, resolve_entity), but there are clear deviations: bare single-word verbs (remember, recall, forget, subscribe, unsubscribe), prefix-family names (pipeworx_feedback, pipeworx_trending; polymarket_edges, polymarket_fill_risk), and a disjoint ArcGIS trio (layer_info, query_layer, search_datasets) that breaks the dominant convention. It is readable but not predictable across the whole set.
34 tools is far beyond what the 'Arcgis Orovalley' purpose warrants — only 3 tools (search_datasets, layer_info, query_layer) actually relate to GIS, with 31 unrelated tools bolted on covering financial data, prediction markets, memory, subscriptions, and AI visibility. This is a sprawling mega-server where an agent must hold an enormous option set in mind; the surface appears to be several platforms fused together rather than one well-scoped toolset.
The genuine ArcGIS surface (search datasets → layer_info → query_layer) is a complete read-only workflow with no dead ends, and the Pipeworx side is impressively comprehensive (routing, grounded answers, research, entity, compare, resolve, validate, memory, subscriptions, feedback). But the tool set as a whole serves no single coherent domain — the declared purpose (ArcGIS Oro Valley data) lacks any write/editing operations, while the majority of the surface addresses unrelated concerns, so 'complete' only applies to one small slice of the 34 tools.