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?
The description adds significant behavioral context beyond annotations: ownership enforcement, row deactivation rather than deletion, and the availability of historical events via recent_alerts. This details the side effects and constraints that annotations do not cover.
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 two sentences, front-loaded with the core action, and each sentence adds valuable information. No filler or 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 single-parameter tool with annotations and no output schema, the description covers purpose, constraints, and side effects, and even connects to recent_alerts for understanding history preservation. It is sufficiently complete for an agent to select and invoke the tool correctly.
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 (100%) for the id parameter, including its format and origin. The description adds no additional parameter meaning beyond referencing 'id' and ownership, 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 tool cancels a subscription by id, with a specific verb and resource. It distinguishes from siblings like subscribe and list_subscriptions by specifying the cancellation action and the id-based target.
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 about ownership enforcement and the deactivation behavior, which implies when to use it (cancelling your own subscription). However, it does not explicitly state alternatives or when not to use it, though the sibling list includes subscribe for the opposite action.
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 tool families blur together: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all route questions to the same underlying data sources for slightly different modes, and the six polymarket_* tools plus bet_research all orbit prediction-market opportunity-finding. The descriptions are detailed, but an agent would need to read deeply to reliably distinguish them.
Names are all readable snake_case and some clusters are consistent (ask_pipeworx*, polymarket_*, pipeworx_*), but the set mixes verb-first names like create_qr and validate_claim with noun-first names like entity_profile, recent_alerts, polymarket_edges, and pipeworx_trending. There is no single predictable naming convention.
33 tools is above the 25+ threshold and reads as a full platform rather than a focused tool. For a server labeled Qrcode, only two tools are QR-related, so the count is severely inflated even if the data-research breadth is defensible.
The Pipeworx data-research surface is fairly complete: query, grounded verification, entity profiling, comparisons, recent changes, discovery, memory, and subscriptions are all represented. But the QR domain for the stated server purpose is only create/read with no batch, styling, or management, and the overall set has no coherent domain to be complete against.