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, so the description adds context about the return fields and the default filtering to active subscriptions, which is useful beyond the 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 sentences: first states the purpose and return fields, second gives usage guidance. No wasted words, well-structured.
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 a simple optional parameter, no output schema, and safety annotations, the description is complete enough for an agent to understand what the tool returns and when to use it.
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 covers the single parameter fully (100% coverage) with a description, and the description does not add additional meaning beyond what the schema provides. Baseline is 3.
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 'List' and the resource 'the caller's active subscriptions', and it distinguishes itself from sibling tools like subscribe and unsubscribe by specifying the action and return fields.
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?
Explicit usage guidance is provided: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This tells the agent when this tool is appropriate.
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.
Many tools have overlapping purposes (multiple ask_pipeworx variants, several polymarket tools, and similar research functions). The agent would struggle to pick the right tool among 36, especially with unclear boundaries between some tools.
Tool names lack a consistent pattern. The li_ prefix for LinkedIn tools is isolated; other tools use random prefixes (pipeworx_, polymarket_) or no prefix at all (ask_pipeworx, bet_research). Mix of verb_noun and noun structures.
36 tools for a server named 'Linkedin_ads' is excessive. Only 5 tools are actually LinkedIn ads-related; the rest are unrelated, making the server feel bloated and unfocused. A focused server would have 5-10 tools.
For the stated LinkedIn ads domain, the tool set is incomplete: missing create/update/delete campaigns, budget management, and targeting. While the server includes many other data tools, its advertised purpose is poorly served.