product_searchBranded product search.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| number | No | ||
| offset | No | ||
| maxCalories | No | ||
| minCalories | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, providing a clear safety profile. The description adds no behavioral context beyond 'branded'—no mention of result behavior, limits, or data source. It fails to build on the 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?
At two words, the description is under-specified rather than concise. It is short but lacks necessary details, so it fails to earn its place as a functional description.
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 a 5-parameter tool with no schema descriptions, the description is severely incomplete. It does not explain search behavior, filter semantics, or how it differs from sibling tools. The output schema exists but does not compensate for the lack of textual context.
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 description coverage is 0% and the description provides no explanation of the parameters. The schema only includes an example, not semantic descriptions. The description does not help the agent understand what number, offset, maxCalories, or minCalories mean.
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 'Branded product search' identifies the tool as a product search function, adding the modifier 'branded' which gives some specificity. However, it is vague about what 'branded' means and does not distinguish it from sibling tools like product_information or ingredient_search. It stops just short of being a tautology.
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?
There is no guidance on when to use this tool versus alternatives. No use cases, prerequisites, or exclusions are provided. The agent is left to guess based on the tool name alone.
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.