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 and destructiveHint=false, so the bar is lower. The description adds value by specifying the return fields (id, type, params, created_at, last_fired_at, fire_count) and clarifying the caller-scoped nature of the listing, which is context beyond the annotations.
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 concise sentences: first states purpose, second lists return fields and gives usage direction. No filler, well front-loaded.
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 full schema coverage, safety annotations, and a description that covers return fields and usage scenarios, all necessary context is provided. No gaps remain for the agent to 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 description coverage is 100% with include_inactive fully described. The description adds no parameter-specific details, but the schema already carries that burden, so the baseline 3 applies.
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 'List the caller's active subscriptions' with a specific verb and resource, and distinguishes from sibling tools like subscribe/unsubscribe by specifying caller-scoped active subscriptions and the exact return fields.
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 provides explicit when-to-use guidance: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This helps the agent choose this tool over alternatives and connects it to the unsubscribe workflow.
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 tool clusters overlap heavily: three ask_pipeworx variants, five polymarket_* tools, and two AI-visibility tools (ai_visibility_check vs scan_competitor_ai_presence) could easily be misselected. While descriptions are detailed, the boundaries between search/research/bet/compare tools are blurry enough to cause agent confusion.
Tool names follow a consistent lowercase snake_case pattern, and most use a verb-first or noun-based descriptive style (ask_pipeworx, bet_research, entity_profile, validate_claim). Minor deviations like deep_research or process_v2 are absent here; the set is largely predictable and readable.
At 32 tools, the server exceeds the 25-tool threshold for heaviness. Many tools are edge-case variants or meta-features (pipeworx_feedback, pipeworx_trending, suggest_questions) that could be consolidated. The server's stated identity as 'Victorian Complaint' also clashes with this scale, making the count feel excessive for the apparent core purpose.
The tool set provides broad coverage for data research, entity resolution, comparison, memory, subscriptions, and prediction-market analysis. It supports query, research, discover, validate, and monitor workflows with few dead ends. Minor gaps like a generic 'get_entity' or direct data-writing tools exist, but they are not core to the implied domain.