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). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so safety is covered. The description adds valuable context by scoping to the caller's subscriptions and disclosing the exact return fields (id, type, params, created_at, etc.). This goes beyond the annotations and helps the agent predict output structure.
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 with no filler. The first sentence states the purpose and return fields; the second gives actionable usage guidance. Every word earns its place.
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 having no output schema, the description lists all return fields, so the agent knows exactly what to expect. The single optional parameter is fully documented in the schema. The use case is clearly tied to sibling operations (subscribe/unsubscribe), making this complete for a simple list tool.
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?
There is one optional parameter (include_inactive) with a clear English description in the schema covering 100% of parameters. The tool description does not mention this parameter, but it's consistent with the phrase 'active subscriptions' (implying inactive excluded by default). The schema does the heavy lifting, so baseline 3 is appropriate.
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 uses a specific verb 'List' and clearly identifies the resource as 'the caller's active subscriptions'. It differentiates from sibling tools like subscribe/unsubscribe by focusing on listing existing subscriptions. Including the return fields adds further clarity.
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 this tool: 'before adding more' or 'to find an id to cancel', which implies alternatives (subscribe/unsubscribe). This gives the agent clear contextual guidance on when this list tool is appropriate versus modifying subscriptions.
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 tool groups have overlapping purposes (e.g., ask_pipeworx, ask_pipeworx_grounded, deep_research all query Pipeworx data; polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker all analyze prediction markets). Descriptions help distinguish them, but the boundaries are not always clear.
Tool names are mostly descriptive but mix conventions: some are verb_noun (list_subscriptions, get_prizes_by_year), others are noun_verb (pipeworx_feedback, polymarket_edges), and a few are single verbs (remember, recall). No strong pattern, but still readable.
With 32 tools, the server covers a wide range of domains (data querying, prediction markets, company analysis, Nobel prizes, memory, subscriptions). The count is high but each tool serves a specific purpose, justifying the breadth.
The tool set provides comprehensive coverage for its stated domains: data retrieval, entity resolution, comparison, monitoring, and memory. Minor gaps exist (e.g., no direct bet placement on Polymarket), but core workflows are well-supported.