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=true, idempotentHint=true, and destructiveHint=false, so safety is covered. The description adds value beyond annotations by specifying that results are scoped to 'the caller's active subscriptions' and by listing the exact response fields, giving the agent a clear expectation of the return payload and default behavior.
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 and front-loaded with the primary action. The first sentence states the verb and resource, the second adds return fields and usage guidance. Every sentence earns its place; there is no fluff or repetition of schema/annotation 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?
This is a simple list tool with one optional parameter and no output schema. The description compensates for missing output schema by listing return fields. It also provides clear user intent (review before adding, find ID to cancel) and default behavior. For its complexity, the description is complete and self-contained.
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 only parameter (include_inactive) has a description in the schema. The description does not mention the parameter but reinforces the default behavior by saying 'active subscriptions.' Per the rubric, with high coverage the baseline is 3; the description does not add significant new meaning 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 begins with a specific verb+resource: 'List the caller's active subscriptions.' It clearly distinguishes from siblings like subscribe/unsubscribe by focusing on listing, and it enumerates the exact return fields (id, type, params, etc.), leaving no ambiguity about the tool's function.
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 explicitly states when to use the tool: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This directly connects to sibling tools (subscribe/unsubscribe) and provides actionable context. It also implies the default scope (active subscriptions) and the purpose of finding IDs for cancellation.
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 have overlapping purposes, e.g., multiple 'ask' tools (ask_pipeworx, ask_pipeworx_grounded, deep_research) and multiple 'compare' tools (compare_entities, scan_competitor_ai_presence). Descriptions are verbose but often don't clearly distinguish when to use each, causing confusion.
Naming is highly inconsistent: snake_case (ai_visibility_check), camelCase (polymarket_fill_risk), and arbitrary prefixes (scan_, generate_, etc.). No consistent verb_noun pattern; e.g., 'query_layer' vs 'search_datasets' vs 'layer_info' all involve data retrieval but use different patterns.
33 tools is excessive for a server purportedly focused on ArcGIS Carlsbad. Many tools are unrelated (e.g., polymarket betting, npm package scanning). The core ArcGIS functionality could be covered by 3-5 tools, but the server is bloated with Pipeworx utilities.
The ArcGIS portion lacks update/delete capabilities and is limited to querying. The Pipeworx tools cover a broad range of data sources but introduce many dependencies and meta-tools, creating a cluttered surface with dead ends (e.g., tools requiring paid accounts without fallback).