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 provide readOnlyHint=false, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds valuable context: ownership is enforced, the row is deactivated not deleted, and historical events remain accessible via recent_alerts. This clarifies the tool's actual behavior beyond the annotations, though it does not expand on potential side effects hinted by openWorldHint.
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 concise with three sentences, each serving a purpose: stating the action, specifying ownership constraints, and clarifying the result. It is front-loaded with the core purpose and avoids unnecessary details.
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?
Given the tool's simplicity (single parameter, no output schema), the description covers the action, side effects, ownership rules, and relation to recent_alerts. It could optionally mention the return value or confirmation behavior, but the current content is largely complete for effective usage.
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 with a detailed description for the required 'id' parameter. The tool description only mentions 'by id' which adds no new meaning. Baseline score of 3 is appropriate as the schema already handles parameter semantics adequately.
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 verb 'Cancel' and the resource 'subscription by id', providing a specific and unambiguous purpose. It also hints at differentiation from sibling tools like 'subscribe' (creation) and 'recent_alerts' (historical events), making the tool's role distinct.
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 mentions ownership enforcement ('you can only cancel your own subscriptions') and the non-destructive nature (deactivation not deletion), directing users to 'recent_alerts' for history. While it gives clear context for when to use the tool, it lacks explicit exclusions or alternatives beyond the sibling reference.
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 tools overlap: ask_pipeworx and ask_pipeworx_beta are currently identical, and the five prediction-market tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_edge_tracker) cover adjacent tasks that require careful reading. However, descriptions are unusually explicit about when to prefer each, and non-overlapping domains (memory, subscriptions, earthquakes, npm) are clearly separated.
All tool names are snake_case with a mostly verb-first pattern (ask_, compare_, discover_, generate_, list_, scan_, search_, validate_), making the surface predictable. Minor deviations like deep_research, entity_profile, and single-word verbs (remember, recall, forget) break the pattern slightly, but each family is internally consistent.
33 tools exceeds the 25+ threshold for 'too many,' and several could be consolidated — ask_pipeworx_beta is redundant today, and the prediction-market suite could fold into 2-3 tools. The count reflects a genuinely wide data platform with meta-tools (discover_tools, suggest_questions, ask_pipeworx) already covering discovery, so the surface feels heavy for an agent to triage.
Core workflows are well covered: querying (ask_pipeworx, grounded, deep_research), research profiles (entity_profile, compare_entities, recent_changes), input resolution (resolve_entity), fact-checking (validate_claim), memory lifecycle, and subscription lifecycle all have complete loops. Minor gaps exist, notably no tool to fetch a pipeworx:// citation URI directly (search_within expects already-fetched text), and some one-off tools like generate_llms_txt and scan_dependency feel bolted on.