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, idempotentHint, and destructiveHint false, establishing the tool as safe. The description adds beyond this by detailing the return fields and default behavior (only active subscriptions), which is valuable context without contradicting 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 concise, consisting of three sentences. It front-loads the action and return fields, uses imperative voice, and contains no extraneous information. Every sentence adds value.
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 list tool with one optional parameter and no output schema, the description adequately covers the return structure, scope (caller's subscriptions), and usage. It does not mention pagination or limits, but for this tool, the description is sufficiently complete given the annotations and simple nature.
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 input schema has 100% coverage for the single boolean parameter include_inactive. The description does not add new meaning beyond the schema, as it only implies the default by saying 'active subscriptions'. Baseline of 3 is appropriate since schema covers the parameter.
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, specifies the returned fields (id, type, params, created_at, last_fired_at, fire_count), and provides use cases (review before adding or find id to cancel). This distinguishes it from sibling tools like subscribe and unsubscribe.
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 states when to use the tool: to review subscriptions before adding more or to get an id to cancel. While it does not explicitly list when not to use it or mention alternatives, the context is clear and sufficient for an agent to decide.
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.
Several tools have genuinely unclear boundaries: ask_pipeworx and ask_pipeworx_beta are currently identical in behavior, and the polymarket_edges / polymarket_arbitrage / polymarket_kalshi_spread trio all scan for mispricings with overlapping descriptions. The rich usage notes help, but they cannot fully rescue a set where two tools literally do the same thing right now.
The majority of tools use readable snake_case, but conventions are mixed: verb-first names (get_index_data, resolve_entity, validate_claim) sit alongside noun-first names (catalog_browse, index_catalog, entity_profile, bet_research), standalone verbs (remember, forget, subscribe), and adjective-led names (recent_alerts, deep_research). The pattern is predictable within clusters but not uniform across the set.
35 tools is well above the comfortable range, and the set spans many unrelated domains—CBS Israel statistics, Pipeworx data routing, Polymarket betting, memory, subscriptions, npm dependency scanning, and AI visibility checks. Even if each tool has a purpose, the surface is bloated and poorly scoped for a server named 'Cbs Il'.
For a general data-access gateway, the set covers the major workflows: routing questions, grounded verification, deep multi-source research, entity profiles, comparisons, subscriptions, memory, and tool discovery. Minor gaps exist, such as no direct raw-record fetch without routing and no keyword search over the CBS catalog, but agents can work around these.