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 ('The row is deactivated (not deleted) so its historical events stay available via recent_alerts.'). This adds valuable context not present in the annotations, and it does not contradict the 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 earning its place: it states the action, the ownership constraint, and the deactivation side-effect. There is no unnecessary filler, and it is well-structured and front-loaded.
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 mutation tool with one parameter and no output schema, the description covers essential aspects: action, ownership, and data persistence behavior. It does not describe return values or error cases, but given the simplicity, this is a minor gap.
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 schema already provides full coverage for the 'id' parameter, describing it as a UUID returned by subscribe. The description merely says 'by id' and adds no additional meaning beyond the schema, 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 with a specific verb and resource: 'Cancel a subscription by id.' It distinguishes itself from sibling tools like 'subscribe' and 'list_subscriptions' by focusing on the cancellation action and explicitly noting the deactivation behavior.
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 for when to use the tool: to cancel a subscription. It also adds ownership constraints ('you can only cancel your own subscriptions'), which is a useful usage guideline, though it does not explicitly name alternatives or exclusion scenarios.
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 tools have nearly identical or heavily overlapping purposes: ask_pipeworx and ask_pipeworx_beta are explicitly identical, and the six Polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread, bet_research) all involve finding/analyzing prediction-market opportunities. deep_research and ask_pipeworx also overlap as general query routers, and ai_visibility_check vs scan_competitor_ai_presence is another confusable pair.
All names use snake_case and are descriptive, but verb placement is inconsistent: some are verb-first (check_domain, compare_entities, resolve_entity), others are verb-last or noun-like (ai_visibility_check, entity_profile, pipeworx_trending, bet_research). There is no chaotic camelCase mix, but the pattern is not predictable enough to guess a tool's behavior from its name.
33 tools is far above the typical well-scoped range and the set spans multiple unrelated domains (data lookup, prediction markets, AI visibility, memory, subscriptions, email/domain validation) that have no cohesive purpose under the 'disify' name. Most tools are not related to domain or email checking, making the count feel like a grab bag rather than a focused toolkit.
For a server named 'disify', the core domain-validation surface is minimal (only check_domain and validate_email) and misses obvious operations like WHOIS lookup or breach/debounce checks. As a general data toolset it is broad but shallow in each area, with gaps such as entity_profile only supporting US public companies and no update/delete operations for most data resources.