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, openWorldHint, idempotentHint, and destructiveHint, covering the safety profile. The description adds that the tool returns the caller's own subscriptions, not global ones, and explicitly lists the output fields. This provides valuable context beyond the annotations, though it doesn't mention any additional behavioral nuances like pagination limits.
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 long, front-loaded with the core action and resource, followed by return fields and usage guidance. Every word earns its place; there is no redundancy or fluff.
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 fully covers what the tool does, what it returns, and when to use it. Combined with the rich annotations and complete schema, there are 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 only parameter, include_inactive, is fully described in the schema, so schema coverage is 100%. The description adds no extra meaning about the parameter, but the schema already explains it well. Baseline of 3 is appropriate since the schema does the heavy lifting.
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 uses a specific verb 'List' with a clear resource: 'the caller's active subscriptions'. It also lists the return fields, which further clarifies what the tool does. This distinguishes it from sibling tools like subscribe and unsubscribe, which are mutation operations.
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 provides clear usage context and implies alternatives (subscribe/unsubscribe) without naming them directly, but the guidance is strong and actionable.
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 overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions to the same underlying data catalog, and the polymarket_* tools have closely related edge/arbitrage/fill-risk purposes. The descriptions are detailed, but an agent still faces real selection risk between near-duplicate query entry points.
The set is consistently snake_case but otherwise mixes conventions: n8n_ and polymarket_ prefixes, bare verbs like remember/forget/recall, noun phrases like recent_alerts, and brand-style names like ask_pipeworx. It is readable but lacks a unified naming system across the server.
34 tools is a heavy surface, especially for a server named N8n where only 3 tools actually relate to n8n workflow management. Most tools serve Pipeworx data lookup, prediction markets, memory, and subscriptions, making the server feel like several different products merged into one.
The data research, entity resolution, memory, and subscription surfaces are well covered, including useful meta-tools for discovery and grounding. However, the n8n portion is read-only with no create/update/delete/run workflow tools, and one-off utilities like generate_llms_txt and scan_dependency sit isolated, leaving the server's overall domain incomplete.