recipe_randomFetch one or more random Spoonacular recipes, optionally filtered by comma-separated tags (e.g. "vegetarian,dessert") and number of results. Returns full recipe details including ingredients and instructions.
| Name | Required | Description | Default |
|---|---|---|---|
| tags | No | ||
| number | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, non-destructive hints. The description adds that the tool returns 'full recipe details including ingredients and instructions,' which is useful behavioral context about the response shape. It does not contradict annotations, and no extra warnings are necessary given the read-only nature.
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?
The description is two sentences long, front-loads the core action, and includes a concrete example of tag formatting. Every sentence adds value without any fluff or redundancy.
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?
For a tool with only two optional parameters and an output schema present, the description fully covers the purpose, parameter semantics, and return content. It's sufficient for an agent to know when to use it and how to invoke it correctly.
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?
The schema has no descriptions (0% coverage), but the description compensates by explaining both parameters: `tags` are comma-separated and demonstrated with an example, and `number` is the number of results. This gives the agent enough semantic understanding to construct valid input.
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?
Description uses 'Fetch' as a specific verb and identifies 'random Spoonacular recipes' as the resource, distinguishing it from sibling recipe tools like recipe_search or recipe_information. It clearly states the optional filters and returns. This makes the tool's purpose explicit and unique.
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 clearly indicates the tool's primary use case—fetching random recipes—with optional tag and count filtering. However, it does not explicitly mention when to prefer this over recipe_search or other recipe query tools, nor does it state any exclusions. The 'random' keyword implies the context, but no alternative tools are named.
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.