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 and destructiveHint=false, so the description adds value by listing the return fields (id, type, params, etc.) and explaining the effect of the 'include_inactive' parameter. This enriches the agent's understanding beyond 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?
Two sentences: first states the action and return fields, second provides usage advice. No redundancy, every sentence 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 the return fields. It also explains the single parameter's default behavior. For a simple tool with good annotations and schema coverage, the description is complete enough for effective use.
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 has 100% description coverage for the single parameter ('include_inactive'), so the description does not add new semantics. It implicitly references the parameter by mentioning 'active subscriptions' but does not provide additional meaning.
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 it lists the caller's active subscriptions, specifying the action ('list'), the resource ('subscriptions'), and the scope ('caller's active'). It distinguishes from siblings like 'subscribe' and 'unsubscribe' by stating its purpose as a review tool.
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 says 'Use this to review what you're monitoring before adding more or to find an id to cancel,' providing clear when-to-use guidance and implying when not to use (e.g., when you need to create or delete 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.
ask_pipeworx, ask_pipeworx_beta (currently identical in behavior), and ask_pipeworx_grounded overlap heavily, and the six-tool Polymarket cluster (edges, arbitrage, fill_risk, edge_tracker, kalshi_spread, bet_research) requires careful reading to distinguish. Descriptions are detailed, but several tools present real selection ambiguity.
Nearly all tools use snake_case with a mostly verb-first or resource-first pattern (ask_, list_, fetch_, read_, subscribe, validate_claim). Minor deviations like entity_profile and recent_changes break the verb-noun pattern slightly, but the overall naming is predictable.
34 tools is excessive for the 'Science Feeds' name, which implies a narrow feed-reading service; only 3 tools actually relate to feeds. The rest form a broad Pipeworx grab bag (memory, npm scanning, AI visibility, prediction markets, feedback), making the set feel unfocused and overweight.
For the broad query/research domain the descriptions actually establish, coverage is strong: discovery, single lookups, grounded/refusal-safe answers, deep research, entity resolution, comparison, change feeds, claim validation, subscriptions, memory, and feedback are all present. The literal science-feed surface is thin, but the toolkit as a whole has few dead ends.