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, etc. Description adds specific return fields (id, type, params, timestamps, fire_count), providing useful behavioral detail 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, front-loaded with the action and output, with no wasted words. Efficient and clear.
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?
Given no output schema, the description explains return fields and usage context. It fully covers agent needs for this simple read operation.
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 covers the single optional parameter 'include_inactive' with a clear description. The tool description does not add further parameter semantics beyond what the schema already provides.
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 return fields. Distinguishes from siblings like subscribe/unsubscribe by describing its role in reviewing current subscriptions.
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 tells when to use: 'to review what you're monitoring before adding more or to find an id to cancel.' This provides clear context and alternatives.
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.
Multiple tools have overlapping purposes, such as ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and specialized tools like entity_profile or validate_claim that can answer similar questions. This creates ambiguity for an agent trying to select the correct tool.
All tool names follow a consistent snake_case pattern, with most using a verb_noun structure (e.g., ask_pipeworx, compare_entities, resolve_entity). There are no mixed conventions or chaotic naming.
With 31 tools, the server is on the heavy side. While each tool has a distinct purpose, the number is borderline for a coherent set and could be streamlined, especially given the overlapping functionality.
The tool set covers a wide range of query and analysis tasks, including data lookup, comparison, betting research, memory, and subscriptions. However, there are notable gaps (e.g., no update/delete for most data, no user management) and some tools seem out of place (e.g., generate_llms_txt).