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 discloses key behaviors beyond annotations: ownership enforcement (only own subscriptions) and soft-delete semantics (deactivated not deleted). These add significant value over the schema and annotations, which only indicate mutation and idempotency.
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, front-loaded with the core action. Every sentence earns its place, covering action, ownership, and the deactivation side effect without wasted words.
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 tool with one parameter and no output schema, the description covers action, ownership, and side-effect. It doesn't mention the return value, but that's not required without an output schema. Minor gap: no mention of error behavior, but the overall context is sufficient.
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?
Schema coverage is 100%, and the schema already describes the id as 'Subscription id (uuid) returned by subscribe.' The description simply says 'by id' without adding new parameter meaning, so 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 states a specific verb+resource: 'Cancel a subscription by id.' It clearly distinguishes from siblings like subscribe (opposite), list_subscriptions (listing), and recent_alerts (viewing history).
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 provides clear context: use this to cancel a subscription by ID. It also notes ownership restrictions and that deactivation preserves history for recent_alerts, giving useful context for when to use it. However, it doesn't explicitly exclude alternatives or mention when not to use 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.
Tools have mostly clear distinctions: ask_pipeworx variants differ by grounding/evidence guarantees, and meta-tools (discover_tools, suggest_questions) serve onboarding. However, ask_pipeworx_beta currently matches ask_pipeworx exactly, creating transient ambiguity, and deep_research vs ask_pipeworx overlap in routing capability though with different scopes.
All tools use snake_case and most follow verb-first naming (ask_pipeworx, compare_entities, generate_avatar, subscribe). A few are descriptive nouns (recent_alerts, recent_changes, pipeworx_trending) but still readable and predictable. No mixed conventions; overall consistent style.
33 tools is excessive for a single server, and many are auxiliary (avatar generation, memory, feedback) that do not serve the core data-access purpose. The primary question-answering capability is centralized in a few routers, making many separate tools feel redundant or unrelated, which dilutes navigability.
The core domain of structured data access is well covered with routing, grounded answering, deep research, entity profiles, comparisons, and claim validation. Subscription lifecycle (subscribe/unsubscribe/alerts) and memory (remember/recall/forget) round out the surface. Minor gaps like lack of direct tool invocation outside the router are covered by discover_tools, and no critical dead ends exist for typical queries.