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, deactivation (not deletion), and linkage to recent_alerts. Annotations already indicate non-readonly and non-destructive, which the description reinforces without contradiction.
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 action, and every sentence adds value. No 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 ownership, deactivation, and reference to recent_alerts. It could mention error handling for invalid IDs, but overall it 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 parameter as 'Subscription id (uuid)'. The description does not add new parameter-level details beyond the verb, so it meets the baseline for high coverage.
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', a specific verb+resource. It distinguishes from siblings like subscribe and list_subscriptions by indicating ownership enforcement and deactivation behavior.
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 explains when to use the tool and provides context on ownership enforcement and deactivation, which guides selection against alternatives. It implies that for historical data, recent_alerts should be used, but does not explicitly list when not to use this tool.
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.
Multiple research/query entry points overlap heavily: ask_pipeworx and ask_pipeworx_beta are currently identical, ask_pipeworx_grounded and deep_research sit on the same routing core, and validate_claim/bet_research/entity_profile all wrap lookup-and-analyze behavior. The detailed descriptions help within specialized clusters, but the central ask_pipeworx family alone creates real selection ambiguity.
All names are lower_snake_case and several families are consistent (ask_pipeworx_*, polymarket_*, remember/recall/forget), but the overall set mixes bare verbs, nouns, and verb_noun composites with no global pattern (disease, metadata, query, entity_profile, generate_llms_txt, validate_claim). It is readable but not predictable across the full 34-tool surface.
34 tools is over the 25+ threshold and the set bundles several distinct domains—disease ontology, Pipeworx data access, prediction markets, AI visibility, npm scanning, memory, and subscriptions—into one server. Each subfamily may be justified, but the combined surface is heavy and makes tool selection harder than the underlying tasks require.
The disease domain has query/disease/metadata for search-and-fetch read coverage, and the broader research side has lookup, grounded verification, deep research, entity profiles, comparisons, subscriptions, and memory lifecycle tools. Minor gaps exist (no direct tool to fetch pipeworx:// citation URIs, no disease browsing/pagination), but these are workable rather than blocking.