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 indicate non-read-only, idempotent, and non-destructive. The description adds value by disclosing that the operation deactivates rather than deletes, preserving historical events. No contradictions with 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 concise sentences that front-load the core action ('Cancel a subscription by id') and then add key constraints and behavior. No 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 cancellation tool with one parameter, the description provides sufficient context: what it does, ownership constraint, side effects (deactivation), and relation to 'recent_alerts'. The annotations cover safety traits. Complete for an agent's needs.
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 has full coverage for the single 'id' parameter, describing it as 'Subscription id (uuid) returned by subscribe.' The description adds contextual ownership enforcement but does not provide additional parameter-level semantics beyond the schema.
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, using a specific verb ('Cancel') and resource ('subscription'). It distinguishes itself from sibling tools like 'subscribe' and 'list_subscriptions'.
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 notes ownership enforcement ('you can only cancel your own subscriptions'), which tells the agent when the tool is usable. It also mentions that the row is deactivated rather than deleted, linking to 'recent_alerts' for historical data. However, it does not explicitly state when not to use this tool or compare with alternatives.
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 clearly described distinct purposes, but several overlap: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, and ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all occupy neighboring query/discovery territory. The Polymarket and memory tool families, by contrast, are well differentiated.
All names are snake_case and readable, but the set mixes verb-first names (ask_pipeworx, compare_entities, resolve_entity, validate_claim) with noun-phrase names (entity_profile, bet_research, recent_alerts, polymarket_arbitrage) and brand prefixes (pipeworx_*, polymarket_*). There is no consistent verb_noun pattern across the toolkit.
34 tools is well beyond the heavy range, and the count is especially inappropriate because the server is named Kegg but only find, get_entry, and list_database relate to KEGG bioinformatics. The remaining 31 tools span unrelated domains (generic data research, prediction markets, memory, subscriptions, AI visibility, npm scanning), making the scope feel like several products merged into one.
As a KEGG server, the surface is severely thin: three read-only tools with no pathway mapping, sequence search, or cross-reference utilities. The Pipeworx research and Polymarket betting subsystems are more complete, but their presence under a Kegg server makes the overall surface incoherent rather than complete.