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 indicate non-readonly, idempotent, and non-destructive behavior. The description adds valuable context beyond these: ownership is enforced, rows are deactivated not deleted, and historical events remain available. This clarifies the operational semantics without contradicting 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 short sentences, with the action front-loaded and no redundant information. Every sentence contributes a distinct detail: what it does, who can do it, and what happens to the data.
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 mutation tool, the description covers the essential behavioral aspects: ownership, deactivation instead of deletion, and linkage to recent_alerts. It does not mention the return format, but there is no output schema and the mutation's effects are sufficiently described.
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 provides 100% coverage for the single id parameter, describing it as a uuid returned by subscribe. The description reinforces that the cancellation is by id and references subscribe, but it does not add significant meaning beyond the schema. Baseline 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 starts with 'Cancel a subscription by id', a specific verb and resource that clearly states the tool's function. It distinguishes from siblings like subscribe and list_subscriptions by focusing on the cancellation action and explicitly mentioning ownership enforcement.
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 usage context: it is for canceling own subscriptions, and it notes that the row is deactivated rather than deleted. While it does not explicitly list alternative tools or when not to use, the mention of recent_alerts for historical events implies the intended scope.
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 have distinct purposes, but there is overlap within the ask_pipeworx family (beta, grounded) and Polymarket tools (arbitrage, edges, fill_risk), which could cause confusion. Detailed descriptions mitigate this, but the boundaries are not always clear.
Tool names use snake_case but lack a consistent verb_noun pattern. Some are imperative (discover_tools), others are descriptive (ask_pipeworx, bet_research), and some are noun phrases (entity_profile, recent_alerts). This inconsistency makes it harder to predict tool names.
33 tools is on the higher side for a single server, but the broad scope (company data, prediction markets, memory, etc.) partially justifies the count. However, many tools are variations of core functionality, suggesting possible consolidation.
The tool set covers a wide range of data sources and tasks, including company profiles, comparisons, economic data, and prediction markets. The universal ask_pipeworx router fills most gaps, though some niche data sources might not be directly accessible.