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 include idempotentHint=true and destructiveHint=false. The description adds significant behavioral context beyond annotations: 'Ownership is enforced — you can only cancel your own subscriptions' and 'The row is deactivated (not deleted) so its historical events stay available via recent_alerts.' This fully explains safety and side effects, with no contradiction to 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 short, front-loaded sentences. Each sentence earns its place: purpose, ownership rule, and deactivation behavior. No redundant or low-value content.
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 one-parameter tool with no output schema, the description is complete. It covers the operation, constraints (ownership), consequences (deactivation not deletion), and ties to a related tool (recent_alerts) for historical events. There are no significant gaps.
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 provides 100% coverage for the single parameter, including type, format (uuid), and origin ('returned by subscribe'). The description adds no extra meaning about the id parameter beyond referencing it as 'by id', 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 clearly states 'Cancel a subscription by id' – a specific verb and resource, with the method (by id) and scope (subscription). This distinguishes it from sibling tools like subscribe and list_subscriptions, and further clarifies the behavior as deactivation rather than deletion.
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 context by noting ownership enforcement and that deactivated subscriptions keep historical events available via recent_alerts, implying when this tool is appropriate. However, it does not explicitly name alternatives or state when not to use it, so it falls short of a perfect 5.
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.
Multiple tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are variants of the same router, and the six polymarket_* tools cover overlapping arbitrage/edge analysis territory. ai_visibility_check vs scan_competitor_ai_presence and discover_tools vs suggest_questions add further boundary ambiguity. While descriptions try to differentiate, an agent could easily misselect among these clusters.
Most names are readable snake_case, but there is no consistent verb_noun pattern: verbs vary (ask, get, list, scan, search, suggest, validate, generate, compare) and several tools are named by product prefix (pipeworx_*, polymarket_*) rather than by action. The pattern is predictable within clusters but inconsistent across the set.
33 tools is heavy for the server's stated name, 'Metals Api', which only has two metals-related tools (get_historical, get_latest). Even as a general data-research server, the surface is bloated with memory utilities, subscription management, feedback, trending, and unrelated AI-visibility scanning. The scope mismatch makes the count feel unjustified.
The core metals domain is thin: latest and single-date historical prices exist, but there is no time-series range query, no list of supported metals, and no explicit currency conversion endpoint. The broader data-research/subscription/memory surface is relatively complete, but it is disconnected from the server's apparent purpose, leaving notable gaps for a metals-focused agent.