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 readOnly, openWorld, idempotent, non-destructive. Description adds return fields and mentions active/inactive via parameter context. Does not add significant behavioral details beyond 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?
Two sentences, no wasted words. First sentence states purpose and fields, second provides usage guidance. Efficiently conveys necessary information.
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?
Given single parameter, high schema coverage, and annotations, description covers purpose, fields, and usage guidance. Minor gap: implicitly restricts to caller's subscriptions but doesn't explicitly state that. Otherwise 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%; parameter 'include_inactive' is fully described in schema. Description does not add additional details about parameter format or behavior beyond what schema provides. Baseline 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?
Description clearly states verb (list), resource (subscriptions), and scope (caller's active). Specifies returned fields. No explicit differentiation from siblings like subscribe/unsubscribe, but context of subscription management is implied.
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?
Provides explicit use cases: 'review what you're monitoring before adding more' and 'find an id to cancel.' Lacks exclusions or when-not-to-use, but the guidance is clear 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.
Most tools have detailed 'use when' guidance and the polymarket/entity clusters are distinguishable, but ask_pipeworx and ask_pipeworx_beta are currently identical, and ask_pipeworx/ask_pipeworx_grounded/deep_research sit close together. Many other tools (ai_visibility_check vs scan_competitor_ai_presence, bet_research vs polymarket_edges) require careful reading to keep separate.
Consistent snake_case and recognizable subfamilies (ask_pipeworx*, polymarket_*, get_*) keep names readable. However the overall set mixes verb_noun (get_schedule, resolve_entity), bare verbs (remember, forget), noun phrases (recent_alerts, pipeworx_trending), and compound noun names (bet_research, polymarket_fill_risk), so there is no single predictable convention.
35 tools is well over the 25+ threshold and spans many unrelated concerns: F1 data, universal data lookup, prediction markets, subscriptions, memory, npm scanning, and AI visibility. While a broad data platform can justify a large surface, the mix of one-off and meta tools makes this feel bloated rather than well-scoped.
For a server named F1 the surface is thin: it covers schedule, driver profiles, race results, and driver standings but lacks constructor standings, qualifying, team/circuit data, and a driver list. The surrounding Pipeworx tools provide depth in other domains, but they do not fill the F1-specific gaps.