meal_plan_weekGenerate a Spoonacular 7-day meal plan matching targetCalories, diet, and comma-separated exclude ingredients. Returns meals per day with recipe ids, titles, images, and per-day nutrition totals.
| Name | Required | Description | Default |
|---|---|---|---|
| diet | No | ||
| exclude | No | ||
| targetCalories | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, and the description adds no contradicting claims. It supplements annotations by stating the exact output shape (meals per day with ids, titles, images, nutrition totals) and that the plan matches the given filters, which are useful behavioral details. It does not discuss failure modes or rate limits, but given the strong annotation coverage, this is a solid contribution.
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 followed by the minimal necessary detail on inputs and return format. There is no redundant or filler content.
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 tool is a simple 3-optional-parameter read-only generator with an output schema and strong annotations. The description covers the input filters and result structure adequately. A minor gap is that with required parameters = 0, it does not state defaults or behavior when optional params are omitted, but the included examples partially compensate.
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?
With 0% schema description coverage, the description must carry the parameter semantics. It explicitly names all three parameters (targetCalories, diet, exclude) and specifies that exclude uses comma-separated ingredients, adding real meaning beyond the bare schema. It still leaves some ambiguity around accepted diet values and targetCalories units, but the core semantics are present.
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 opens with 'Generate a Spoonacular 7-day meal plan' – a specific verb and resource – and enumerates the filtering criteria (targetCalories, diet, exclude) plus the output composition. This clearly distinguishes it from generic meal-plan tools like sibling meal_plan_generate by specifying the 7-day span and per-day nutrition totals.
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 implies a weekly-planning use case through '7-day meal plan' and 'meals per day,' but it does not provide explicit when-to-use/when-not-to-use guidance, nor does it name alternatives such as meal_plan_generate for single-day plans. Thus the guidance is only implied.
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.