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 the safety profile is covered. The description adds value by detailing the response fields and implying that only active subscriptions are returned by default, which complements the schema's include_inactive parameter. It does not mention potential pagination or large result sets, but for a simple list tool this is adequate.
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 compact and front-loaded: the primary action in the first clause, followed by the return fields and a practical use case. Every sentence earns its place with no 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 list tool with one optional parameter and no output schema, the description is complete: it explains the return fields, the default behavior, and when to use it relative to sibling tools. Annotations cover safety, and schema covers parameters, leaving no significant gaps.
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?
The single parameter include_inactive is fully described in the schema (100% coverage), including its default behavior. The tool description does not add any semantic information beyond the schema, so the baseline score of 3 is appropriate.
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's function with a specific verb ('List') and resource ('the caller's active subscriptions'), and lists the exact return fields (id, type, params, created_at, etc.), making it easy to distinguish from sibling tools 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?
Explicitly states when to use the tool: 'to review what you're monitoring before adding more or to find an id to cancel.' This clearly contrasts with adding subscriptions (subscribe) and cancelling (unsubscribe), providing practical guidance on when this tool is appropriate.
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.
Multiple tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all answer data questions, while discover_tools and suggest_questions both serve discovery. The three ArcGIS tools are distinct but are drowned out by the unrelated Pipeworx and prediction-market tools, making it hard to pick the right one.
Tool names are mostly snake_case but mix verb_noun (query_layer, search_datasets, remember), noun_noun (layer_info, entity_profile), and less conventional forms (search_within, generate_llms_txt). The naming is readable and not chaotic, but there is no single consistent pattern across the set.
34 tools is excessive for a server named 'Arcgis Palmbeach'. Only 3 tools are GIS-related (search_datasets, query_layer, layer_info); the other 31 are unrelated Pipeworx data, prediction-market, and memory utilities. The count grossly mismatches the server's apparent purpose.
For the stated ArcGIS/Palm Beach County GIS purpose, the tool surface is severely incomplete: only search, query, and layer-schema lookup exist, with no data editing, feature operations, or map-service management. While the Pipeworx domain is heavily covered, that is not what the server name promises, so the surface is a poor fit.