ingredient_informationFetch details for a Spoonacular ingredient by numeric id. Returns name, category, possible units, and nutrition data for the given amount and unit. Use ingredient_search to get the id first.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ||
| unit | No | ||
| amount | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description doesn't need to restate safety. It adds behavioral context about the data returned (name, category, possible units, nutrition data) and the dependency on having the correct id. This is useful beyond 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?
The description is two sentences, front-loads the main action, and includes a practical workflow tip. Every word adds value without 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 3 parameters, strong annotations, and an output schema, the description covers the essential points: what it does, what it returns, and how to obtain the required id. It also correctly differentiates from the sibling tool. No critical information is missing.
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 0% description coverage, so the description carries the burden. It clarifies that 'amount' and 'unit' are used to compute nutrition data, and that 'id' is numeric, but it does not fully explain each parameter's format or allowed values. The examples in the schema help, but the description partially compensates without being exhaustive.
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 uses the specific verb 'Fetch' and clearly identifies the resource (Spoonacular ingredient by id). It also distinguishes itself from sibling ingredient_search by implying that this tool retrieves details rather than performing searches, making its scope unambiguous.
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 explicitly states when to use the tool ('Fetch details for a Spoonacular ingredient by numeric id') and provides a direct alternative/guidance: 'Use ingredient_search to get the id first.' This gives clear contextual usage direction.
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.