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. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds behavioral context beyond annotations, such as ownership enforcement and deactivation (not deletion) with historical events retained. Annotations already provide idempotency and non-destructive hints, so the description complements them well.
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 that front-load the core action, then add critical conditions. Every sentence is essential, and 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 simple tool with one parameter and no output schema, the description covers the effect (deactivation) and historical implications. It lacks explicit return value description but is otherwise complete.
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?
With 100% schema description coverage, the baseline is 3. The description does not add further parameter meaning beyond 'by id', but it does not contradict either. No additional value is provided.
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) and resource (subscription by id). It distinguishes from siblings like subscribe and list_subscriptions by specifying ownership enforcement and deactivation behavior, making the purpose unmistakable.
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 explicitly states ownership enforcement ('you can only cancel your own subscriptions'), which guides when the tool is applicable. However, it does not mention alternatives or explicitly 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.
Many tools have overlapping purposes (e.g., three ask_pipeworx variants, multiple Polymarket analysis tools, and several entity-focused tools). Agents may struggle to select the correct tool for tasks like querying data or analyzing prediction markets.
All tool names use snake_case, and most follow a verb_noun pattern (e.g., ask_pipeworx, compare_entities). A few names like ai_visibility_check are slightly less conventional, but overall the naming is consistent.
With 32 tools covering a broad range of data services, the count is on the high side but still manageable. However, the server name 'Idf Events' is misleading, as only one tool relates to events in Paris.
The tool set covers core workflows for the Pipeworx platform: data querying, research, comparisons, subscriptions, memory, and feedback. Minor gaps exist, but most user needs are addressed.