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 provide readOnlyHint, idempotentHint, etc. The description adds the return format and usage context, complementing the annotations without contradiction.
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, front-loaded with purpose, 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?
For a simple read-only list tool with good annotations, the description is complete: it explains what it does, what it returns, and when to use it.
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%, with description for the one parameter. The tool description does not add extra meaning beyond what is in the schema for this 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?
Clearly states it lists the caller's active subscriptions and specifies the returned fields. Distinguishes from siblings like 'subscribe' and 'unsubscribe' by providing context for use.
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 monitored subscriptions before adding more or to find an id for cancellation. Though it doesn't mention alternatives, the context is clear.
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 clearly distinct purposes, but the ask_pipeworx family (standard, beta, grounded) and validate_claim vs ask_pipeworx_grounded may cause some confusion for agents despite detailed descriptions.
All tool names follow a consistent snake_case pattern, and most are verb_noun structures (e.g., ask_pipeworx, compare_entities, resolve_entity), making them predictable and easy to distinguish.
With 32 tools, the set is larger than ideal but well-justified by the broad scope of data sources and functionalities (email verification, SEC/FDA lookups, prediction market analysis, memory, subscriptions).
The tool set covers the full pipeline from data discovery (discover_tools, suggest_questions) to retrieval, analysis, comparison, verification, and monitoring, with no obvious gaps for its intended use cases.