list_subscriptionsList the caller's active subscriptions. Returns id, type, params, created_at, last_fired_at, fire_count for each. Use this to review what you're monitoring before adding more or to find an id to cancel.
| Name | Required | Description | Default |
|---|---|---|---|
| include_inactive | No | Include cancelled subscriptions in the response (default false). |
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 already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds behavioral context by specifying the return fields ('Returns id, type, params, created_at, last_fired_at, fire_count for each') and scoping to 'the caller's active subscriptions,' which goes beyond the annotations. It doesn't describe pagination or ordering, but for a simple list tool this is adequate.
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 and return fields, the second gives usage guidance. It is front-loaded with the key action and outcome, with zero 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 read-only list tool with one optional parameter and no output schema, the description adequately covers purpose, usage, and return fields. It compensates for the lack of an output schema by explicitly listing the returned fields and gives a clear context for use. No significant gaps remain.
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% with the only parameter 'include_inactive' described as 'Include cancelled subscriptions in the response (default false).' The tool description adds no parameter-specific details beyond this, so the baseline of 3 applies. The description's mention of 'active subscriptions' implicitly aligns with the parameter but doesn't add new insight.
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 'List the caller's active subscriptions' and enumerates returned fields (id, type, params, created_at, last_fired_at, fire_count). It distinguishes from sibling subscribe/unsubscribe tools by focusing on a read-only listing operation and providing a concrete use case.
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 says 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This provides clear context on when to invoke the tool, contrasting with the actions of adding or cancelling subscriptions. Though it doesn't name siblings like subscribe/unsubscribe explicitly, the usage guidance is explicit and actionable.
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 serve the same 'ask a question' function (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) with subtle differences that are hard to distinguish. Entity-oriented tools like entity_profile, recent_changes, and compare_entities also overlap on company information, making selection ambiguous.
All tool names are lowercase snake_case and mostly descriptive, with consistent prefixes for tool families (ask_pipeworx, polymarket_*, statuspage_*). However, there is no uniform verb_noun pattern — some are bare verbs (forget, remember, recall) while others are noun phrases (entity_profile, recent_changes) — so predictability is moderate.
With 35 tools, this set is heavily overloaded for a server named Statuspage; only 4 tools actually relate to status pages, while the rest form a broad data query, prediction market, and memory toolkit. The count is far beyond what the name implies and dilutes focus.
For a Statuspage server, the tool surface is severely incomplete: it only reads status and incidents and provides no way to create, update, or resolve incidents. Even for the broader data-tool domain, there are gaps — no direct record-fetch by ID and no write/update operations for data sources.