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 value beyond annotations by disclosing ownership enforcement, deactivation vs. deletion, and retention of historical events. It does not contradict annotations (readOnlyHint=false, destructiveHint=false, idempotentHint=true) and provides behavioral context that annotations alone 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: the first states the purpose, the second adds constraints and side effects. Every sentence contributes necessary context, with no redundant or extraneous information.
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 the action, ownership rule, deactivation behavior, and linkage to 'recent_alerts.' It is fully sufficient for an AI agent to understand and correctly invoke the tool.
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 describes the 'id' parameter as 'Subscription id (uuid) returned by subscribe.' The description merely says 'Cancel a subscription by id,' which adds no new semantic meaning beyond the schema. With 100% schema coverage, 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 'Cancel a subscription by id,' using a specific verb (cancel) and resource (subscription). It adds context about ownership enforcement and distinguishes from siblings like 'subscribe' and 'list_subscriptions' by clarifying the action and constraints.
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 explicitly states ownership enforcement ('you can only cancel your own subscriptions'), which guides when to use and implies when not to. It also mentions that the row is deactivated, not deleted, linking to 'recent_alerts' for history, but lacks explicit exclusions or alternatives for other scenarios.
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.
Each tool has a detailed description clarifying its precise purpose, and even closely related tools (e.g., ask_pipeworx vs ask_pipeworx_grounded) are clearly differentiated by behavior and use case. No two tools appear to serve the same function.
All names use snake_case, but the structural pattern is inconsistent: many follow verb_noun (compare_entities, resolve_entity), while others are noun-based (entity_profile, polymarket_arbitrage) or single verbs (subscribe, remember). This mix reduces predictability.
With 32 tools, the server is quite large for an MCP server. While many tools are justified by the broad domain coverage, the high number can overwhelm agents and increase cognitive load, making it feel somewhat bloated.
The tool set covers a wide range of domains (company data, prediction markets, news, memory, subscriptions, etc.), and the generic ask_pipeworx and deep_research tools gateways to thousands of data sources, effectively filling gaps. However, some areas lack dedicated tools (e.g., weather, real estate) beyond the generic query.