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 goes beyond the annotations by disclosing ownership enforcement and the deactivation behavior (row is deactivated, not deleted), which aligns with destructiveHint=false and idempotentHint=true. It also explains the side effect on historical events, providing valuable behavioral context not present in 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?
The description is three sentences, each providing distinct information: the action, ownership restriction, and deactivation consequence. It is front-loaded with the primary purpose and contains no filler, making it highly concise and well-structured.
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 essential aspects: what it does, constraints, and side effects. It even references the related tool recent_alerts for historical events, making it complete within its context.
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 'id' parameter, so the description adds limited additional meaning. However, it does specify that ownership is enforced, which adds semantic nuance about the required subscription ID (must be your own), enriching the parameter's meaning beyond the schema description.
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 tool cancels a subscription by ID, using the specific verb 'Cancel' with a resource. It distinguishes from siblings like 'subscribe' and 'list_subscriptions' by focusing on the cancellation action and ownership enforcement, making the purpose unambiguous.
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 context: it is used to cancel subscriptions, with ownership enforced. It does not explicitly state when to avoid using it or name alternatives, but the context is sufficient for an agent to select it over subscribe/list_subscriptions. The note about historical events via recent_alerts adds context on consequences.
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 overlap in purpose (e.g., multiple Polymarket analysis tools, multiple AI visibility tools, ask_pipeworx vs deep_research). Agents will have difficulty choosing the correct tool without deep understanding of subtle differences.
Tool names use a mix of styles (snake_case, descriptive phrases) without a consistent verb_noun pattern. For example, 'ask_pipeworx' and 'bet_research' have different naming conventions. This inconsistency makes it harder for agents to predict tool names.
32 tools is on the high side for a single server. Many tools could be merged (e.g., multiple polymarket tools). The count feels excessive for the scope, causing cognitive load and potential selection errors.
The tool set covers a wide range of domains (prediction markets, company data, fact-checking, etc.) but has notable gaps (e.g., limited entity types for company/drug only). Redundancy in some areas makes the set feel bloated rather than complete.