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, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds context by listing the exact returned fields (id, type, params, created_at, last_fired_at, fire_count) and clarifying the 'caller's active subscriptions' scope, which goes beyond the 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 action and return fields, then provides practical usage guidance. Every sentence earns its place with no redundancy or filler.
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?
With no output schema, the description fully compensates by enumerating the return fields. It also covers the primary use cases (review before adding, find id to cancel) and relies on annotations for safety semantics, making it complete for a simple list tool.
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% for the single optional parameter include_inactive, whose description already explains it includes cancelled subscriptions with default false. The tool description adds little beyond what the schema provides, so the baseline 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 a specific verb ('List') and resource ('the caller's active subscriptions'), clearly distinguishing this from sibling tools like subscribe and unsubscribe. It also specifies the scope (caller-specific, active only) and references the 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 clear usage context: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This implicitly distinguishes it from subscribe/unsubscribe and recent_alerts, though it does not explicitly name alternatives or state when not to use it.
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.
There are several tools with overlapping purposes, especially the ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and the multiple Polymarket analysis tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk) which all deal with market edge/arbitrage in slightly different ways. The cocktail tools are distinct, but the majority of the server is a general data lookup service, making it hard for an agent to quickly choose among the many similar 'research' tools.
Tool names follow mixed conventions: some use verb_noun (get_cocktail, search_cocktails, list_subscriptions), some are noun phrases (entity_profile, bet_research, ai_visibility_check), and some are bare verbs (forget, recall, remember) or awkward compounds (polymarket_arbitrage, scan_competitor_ai_presence). There is no consistent pattern across the set, making predictable naming unreliable.
At 35 tools, the count is high, and the server's stated purpose (cocktails) is completely mismatched with the vast majority of tools, which are about data research, prediction markets, and subscriptions. The cocktail functionality is only 4 tools, while the rest feels like a separate general-purpose data API, so the set is both over-scoped and incoherently bundled.
For the cocktail sub-domain, the tools cover the typical use cases: search by name, get full recipe by ID, list by ingredient, and random cocktail. There's no way to create or update recipes, but that's not expected for a read-only cocktail database. For the broader data/lookup purpose implied by most tools, the coverage is extensive, including routing, grounded answers, entity resolution, and comparison, so no critical dead ends are apparent.