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, openWorldHint, idempotentHint, and destructiveHint. The description adds scope (caller's), default active status, and return field details, providing useful context beyond annotations. It does not mention pagination or rate limits, but the tool's simplicity makes this acceptable.
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 with no redundant wording. It front-loads the purpose and return fields, then provides usage guidance, making it concise and 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?
For a simple read-only tool with one optional parameter and no output schema, the description covers purpose, return values, and usage context. Combined with the rich annotations, it provides sufficient information for an agent to select and invoke the tool correctly.
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 fully documents the only parameter (include_inactive) with 100% coverage. The description does not add additional parameter-specific details beyond the schema, but its emphasis on 'active' subscriptions hints at the default behavior. Baseline 3 applies due to high schema coverage.
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 states a specific action (List) and resource (caller's active subscriptions). It also lists the return fields, making the tool's function concrete and distinguishable from siblings 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 provides explicit usage context: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This clearly indicates when to use the tool, though it does not explicitly name alternative tools.
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 overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve as entry points for data questions, and ask_pipeworx_beta currently behaves identically to ask_pipeworx. Entity-focused tools (entity_profile, compare_entities, recent_changes, ai_visibility_check, scan_competitor_ai_presence) also blur together even with lengthy descriptions.
All tools use lowercase snake_case, which is consistent and readable. There is some variation between verb-first names (discover_tools, resolve_entity) and noun-first names (entity_profile, polymarket_arbitrage), plus the ask_pipeworx variant family, but the pattern is predictable overall.
35 tools is heavy, and the scope sprawls far beyond the 'Rba' server name: only 4 tools relate to the Reserve Bank of Australia, while the rest cover a universal data router, prediction markets, memory, subscriptions, AI visibility, npm scanning, and more. Several meta-tools (ask_pipeworx, deep_research, discover_tools, suggest_questions) duplicate the discovery/routing role, making the count feel inflated.
For the RBA-specific subdomain, coverage is solid: directory lookup, series fetching, cash rate, and exchange rates. For the broader data-research domain most tools imply, coverage is quite comprehensive (routing, grounded answers, entity resolution, fact-checking, monitoring, memory), but the server's stated identity is unclear, and the beta duplicate tool adds noise rather than filling a real gap.