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 and idempotentHint, covering safety. The description adds valuable context by listing exact return fields (id, type, params, created_at, last_fired_at, fire_count), which is useful for agents. No contradictions.
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?
Two sentences, each earning its place. First states purpose and return fields, second gives usage guidance. Front-loaded and efficient with 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?
Despite lacking an output schema, the description fully explains what the tool returns. The optional parameter is documented in schema, and the use case is clearly stated. For a simple list tool with rich sibling context, this is complete.
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% for the single parameter include_inactive, so baseline is 3. The description mentions 'active subscriptions' which implicitly aligns with the default false, but it does not explicitly explain the parameter's meaning beyond the schema. No added value beyond schema, but no shortfall.
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 lists the caller's active subscriptions, which is a specific verb+resource pairing. It also differentiates from sibling tools like subscribe/unsubscribe by focusing on review/listing.
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?
Explicitly states when to use: to review current monitoring before adding more or to find an id to cancel. This provides clear context and aligns with sibling tools, giving implied when-not-to-use guidance.
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 distinct purposes, but there are overlapping families (ask_pipeworx variants, company research tools) that could cause confusion. Descriptions help differentiate, but an agent might misselect without careful reading.
Tools follow snake_case with a verb+noun pattern, but some names are less clear (e.g., 'recall', 'remember' are verbs alone). Overall consistent enough, with minor deviations.
33 tools is on the higher side, but the server covers a wide domain (data retrieval, prediction markets, monitoring). Each tool has a clear purpose, so the count feels appropriate rather than excessive.
The tool surface is remarkably complete: querying, comparisons, monitoring, alerts, memory, arbitrage, edge tracking. There are no obvious gaps for the intended data analytics and prediction market use case.