unsubscribeCancel a subscription by id. Ownership is enforced — you can only cancel your own subscriptions. The row is deactivated (not deleted) so its historical events stay available via recent_alerts.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Subscription id (uuid) returned by subscribe. |
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?
The description adds meaningful behavioral context beyond the annotations: ownership enforcement is an authorization requirement, and 'deactivated (not deleted)' explains the non-destructive nature. While the annotations already provide readOnlyHint=false, idempotentHint=true, and destructiveHint=false, the description supplements with specifics about deactivation and ownership, which is valuable.
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 action, and every sentence adds information. 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 single-parameter mutation tool with strong annotations, the description covers purpose, authorization, and the impact on historical data via recent_alerts. The reference to recent_alerts connects to a sibling tool for context. No output schema is provided, so return values are not a gap. The description is complete for its complexity.
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 input schema fully describes the only parameter 'id' with a clear description ('Subscription id (uuid) returned by subscribe'). The description's phrase 'by id' adds no further semantic detail. Schema coverage is 100%, so the baseline of 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?
The description clearly states the action ('Cancel a subscription by id') and the resource (subscriptions). It distinguishes from sibling tools like 'subscribe' and 'list_subscriptions' by specifying cancellation, and the ownership rule adds a specific constraint.
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 a clear context for use: canceling your own subscriptions only. It also mentions a consequence (historical events remain via recent_alerts) which implies an alternative for accessing history. However, it does not explicitly state when not to use the tool or name alternative tools for managing subscriptions.
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 tool groups have unclear boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, polymarket_edges/arbitrage/bet_research overlap heavily, and scan_competitor_ai_presence merely wraps ai_visibility_check. An agent would frequently have to guess which of several overlapping tools to call.
Most tools use lowercase snake_case, but the set mixes verb-first names (resolve_entity, validate_claim) with noun/service-first compounds (polymarket_edges, pipeworx_trending, ai_visibility_check) and inconsistent suffix semantics (ask_pipeworx_beta vs ask_pipeworx_grounded). The pattern is readable but not predictable.
33 tools is far too many for a server named iplookup; only 2 of 33 relate to IP geolocation. The rest form a sprawling data/prediction-market/memory platform that would be better split into multiple focused servers.
Within the actual described scope (a Pipeworx data platform), coverage is strong: routed lookups, grounded verification, deep research, entity identity/profile/comparison, claim validation, discovery, subscriptions, memory, feedback, trending, and a full prediction-market arbitrage suite. Minor gaps exist (no direct pack-listing tool, no update for memory values), but there are no critical dead ends.