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, covering the safety profile. The description adds value by specifying the caller scope and the exact fields returned (id, type, params, created_at, last_fired_at, fire_count), which is useful 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: the first states the action and return fields, the second gives usage guidance. No unnecessary words, and the most important information is front-loaded.
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?
The description covers purpose, usage, return fields, and caller scope. The optional parameter is documented in the schema, and annotations handle safety. Given the simplicity of the tool, this is complete without needing an output schema.
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% for the single optional parameter include_inactive, with a clear description in the schema. The tool description does not add any additional meaning about parameters beyond the schema's own description, 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 clearly states 'List the caller's active subscriptions' with a specific verb and resource, and enumerates the return fields. It distinguishes itself from sibling tools like subscribe and unsubscribe by focusing on listing existing 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?
The description gives explicit usage scenarios: 'review what you're monitoring before adding more' and 'find an id to cancel.' These imply when to use it relative to subscribe and unsubscribe, providing clear context even if alternatives are not named directly.
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 bundles three unrelated domains, and within them several tools are near-indistinguishable: ask_pipeworx_beta is explicitly identical to ask_pipeworx, ask_pipeworx_grounded overlaps with validate_claim, bet_research/polymarket_edges/polymarket_arbitrage all target betting opportunities, and meal_plan_generate duplicates meal_plan_week. The aspect-specific recipe fetchers (ingredients/nutrition/summary/taste) also blur with recipe_information.
Most tools follow a reasonable snake_case verb_noun pattern (recipe_search, resolve_entity, compare_entities, unsubscribe), and each cluster (recipe_*, polymarket_*, ask_pipeworx*) is internally consistent. However, conventions fragment across clusters — bare verb memory tools (remember, forget, recall), the ask_pipeworx_beta/_grounded suffix family, and the odd generate_llms_txt — so no single predictable scheme governs the whole surface.
49 tools is far too many for a coherent surface, and crucially the count is misaligned with the server's stated identity: only 18 of 49 tools actually belong to the Spoonacular food domain, while 27 are Pipeworx data/prediction-market tools and 3 are generic memory utilities. The server appears to be three products mashed into one.
For the core Spoonacular food domain the surface is reasonably complete — search for recipes/products/ingredients, detail fetchers, meal plans, wine pairing, and unit conversion all exist. But the overwhelming presence of unrelated Pipeworx and memory tools makes the server's actual purpose ambiguous, and gaps are hard to assess when the food tools share the namespace with SEC filings and Polymarket arbitrage.