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 cover read-only, idempotent, and non-destructive behavior. The description adds meaningful behavioral context: it returns only the caller's subscriptions and filters to active ones by default. It also discloses the exact return fields, which is valuable given no output schema.
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 core purpose, followed by return-field details and usage guidance. Every word earns its place with no redundancy.
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 one optional parameter and no output schema, the description fully covers the return structure, default filtering, caller scope, and use cases. Critical information is present; nothing essential is missing.
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 sole parameter include_inactive is fully documented in the schema with its meaning and default. The description adds no further parameter details, so with 100% schema coverage, the baseline score of 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 uses the specific verb 'List' with resource 'subscriptions', scoped to 'the caller's active subscriptions'. It also enumerates returned fields, clearly distinguishing it from sibling tools like subscribe/unsubscribe and other list tools.
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 gives explicit use cases: 'review what you're monitoring before adding more or to find an id to cancel'. This clearly frames when to use the tool, though it doesn't explicitly name alternatives or conditional exclusions.
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.
Most tools have distinct purposes, but subtle overlap exists in the ask_pipeworx variants and multiple Polymarket-related tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_kalshi_spread) which could cause misselection without careful reading of descriptions.
All tool names follow a consistent snake_case pattern with clear verb_noun or noun_verb structures, e.g., ai_visibility_check, compare_entities, resolve_entity. No mixing of conventions.
With 29 tools, the server exceeds the typical ideal range of 3-15 and enters the 'too many' category. While each tool serves a specific purpose, the breadth of functionality could likely be streamlined or consolidated without losing capability.
The tool surface covers a wide range of domains—company research, fact verification, data querying, betting analysis, memory, and subscription management. However, some specialized queries rely on the generic ask_pipeworx tool rather than dedicated endpoints, leaving minor gaps for direct access.