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?
Annotations already mark this as non-destructive and idempotent. The description adds critical details: ownership enforcement, deactivation instead of deletion, and preservation of historical events via recent_alerts. This goes beyond annotation data.
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 with no redundancy; the core action is front-loaded and additional context is woven in efficiently.
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 tool with rich annotations, the description covers all necessary behavioral aspects: action, ownership, side effects, and data retention. No output schema is needed for this cancel operation.
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 documents the id parameter (uuid returned by subscribe). The description adds no new parameter-level semantics, so baseline 3 is appropriate given 100% coverage.
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 explicitly states the action ('Cancel a subscription by id') and differentiates from siblings like subscribe and list_subscriptions by focusing on cancellation. The title reinforces the alert context.
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?
It clearly communicates the tool's role for canceling subscriptions and notes ownership restrictions. While it doesn't explicitly name alternative tools, the context is sufficient for typical use cases; a minor gap is no explicit 'when not to use' statement.
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.
Most tools are organized into clearly differentiated families (ask_pipeworx vs ask_pipeworx_grounded, polymarket_edges vs polymarket_arbitrage), but there are some genuinely ambiguous pairs: ask_pipeworx_beta is currently identical to ask_pipeworx, and search_recalls/recent_recalls, ai_visibility_check/scan_competitor_ai_presence, and bet_research/polymarket_edges all require careful reading to avoid misselection.
The set is consistently lowercase snake_case and contains strong families like ask_pipeworx*, polymarket_*, recent_*, and search_*. However, the naming pattern is mixed: imperative verbs (recall, forget, subscribe), noun phrases (entity_profile, recent_changes, pipeworx_trending), and action prefixes (scan_, generate_, validate_) all coexist, making the overall convention less predictable than a uniform verb_noun scheme.
With 33 tools, the server exceeds the healthy range and spreads across many side domains: data research, prediction markets, memory, subscriptions, npm dependency checks, llms.txt generation, and AI visibility audits. No individual tool feels pointless, but the overall surface is sprawling rather than tightly curated for a single purpose.
The core research workflow is well covered: querying, grounded verification, entity resolution, profiles, comparisons, recent changes, claim validation, deep research, memory, and subscriptions. Minor gaps exist—there is no direct reader for pipeworx:// citation URIs, no tool to update or edit a stored memory, and subscriptions can be created/cancelled but not modified—but agents can work around these.