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?
Beyond annotations, the description discloses ownership enforcement and the soft-delete behavior (deactivated, not deleted) plus the effect on historical events via recent_alerts. This adds rich context that complements the annotations without contradiction.
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?
Three short sentences, each earning its place: the action, the ownership constraint, and the side effect. Front-loaded with the core purpose, no redundancy.
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 single-parameter tool, the description covers purpose, ownership, and side effects. No output schema exists, but the outcome ('cancel') is clear. Slight gap: doesn't explicitly state what happens to future alerts, though 'deactivated' implies it.
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 fully describes the single parameter (id with source and type). The description only implicitly references it via 'by id' and adds no new semantic detail beyond the schema, so the baseline score 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 uses a specific verb ('cancel') and resource ('subscription') with a clear method ('by id'). It also distinguishes itself from sibling tools like subscribe and list_subscriptions, and adds unique behavioral details about ownership and soft-delete.
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 implies usage context: cancel a subscription you own, with ownership explicitly enforced. It doesn't explicitly compare to alternatives like subscribe or list_subscriptions, but the context is clear enough for an agent to know when to invoke 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 serve overlapping purposes (ask_pipeworx, ask_pipeworx_grounded, deep_research, bet_research, compare_entities, entity_profile, recent_changes, validate_claim) all querying Pipeworx data with similar outcomes. An agent would struggle to choose correctly without deep understanding of nuanced differences.
Naming conventions are mixed: snake_case (ask_pipeworx, get_anime), camelCase (generate_llms_txt, pipeworx_feedback), and compound names (polymarket_arbitrage, ai_visibility_check). No consistent pattern across the tool set.
34 tools is excessive for a single server. Many are meta-tools (discover_tools, suggest_questions) or narrowly focused (pipeworx_trending, scan_dependency). The server tries to cover too many domains (anime, financial data, predictions, memory) in one surface.
The anime tools (search, get, top) form a reasonable read-only surface. The Pipeworx query tools are comprehensive but lack obvious data management tools (e.g., listing sources, managing credentials). Memory tools (remember/recall/forget) are isolated. Overall, gaps exist but core workflows are covered.