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 indicate read-only, idempotent, not destructive. Description adds that it returns specific fields and can include inactive subscriptions, offering context 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 action and return list. No filler; every sentence serves a purpose.
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?
No output schema, but description lists return fields. Parameters covered. Complete for a list tool with good annotations.
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%, so description adds limited value beyond the schema. It mentions return fields which help contextualize the parameter, but does not elaborate on parameter semantics.
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?
States 'List the caller's active subscriptions' with specific verb and resource, distinguishes 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?
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 and alternative actions.
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.
The tool set includes multiple overlapping tools (ask_pipeworx variants, many polymarket tools) that serve similar purposes, and there is a sharp domain split between food tools and Pipeworx data tools, making it hard for an agent to choose correctly.
Tool names are inconsistent, mixing verb_noun (search_food), noun_verb (nutrition_analysis), prefixed (pipeworx_feedback, polymarket_arbitrage), and no pattern. Some use underscores, some use whole words.
34 tools is on the high side, and the scope is extremely broad (food, data queries, prediction markets, subscriptions), which could overwhelm an agent, but the number alone is not extreme.
As a food API server, it is missing key features like recipe details, ingredient substitution, or meal planning, while including many extraneous tools. The data analytics tools are extensive but not aligned with the server's stated purpose.