Skip to main content
Glama

Meal Plan Generate

meal_plan_generate
Read-onlyIdempotent

Generate a Spoonacular meal plan for a given timeFrame ("day" or "week") matching targetCalories, diet (e.g. vegetarian/vegan/paleo), and comma-separated exclude ingredients. Returns meals with recipe ids, titles, images, and nutrition summary.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dietNo
excludeNo
timeFrameNo
targetCaloriesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A3.9/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false, covering the safety profile. The description adds value by stating the return fields (recipe ids, titles, images, nutrition summary), but it does not disclose other behavioral aspects like rate limits or error conditions. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the action, and every clause carries meaning. No fluff, no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

It covers purpose, parameters, and return shape, and an output schema exists for further details. The main gap is the lack of guidance on the relationship with the sibling tool meal_plan_week and possible default behaviors (e.g., what happens if timeFrame is omitted). Still, it is substantially complete for a read-only idempotent tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With schema description coverage at 0%, the description fully compensates by explaining all 4 parameters: timeFrame values ('day' or 'week'), targetCalories, diet with examples (vegetarian/vegan/paleo), and the comma-separated format for exclude. This goes well beyond the bare schema and gives the agent everything needed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses specific verbs and resources: 'Generate a Spoonacular meal plan' with explicit parameter scoping for timeFrame, targetCalories, diet, and exclude. It also lists return contents. However, it does not explicitly distinguish itself from the sibling tool 'meal_plan_week', which may also handle week plans, so it misses that sibling differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for meal plan generation but does not explicitly say when to use this tool versus alternatives like meal_plan_week. There are no exclusions or alternative mentions, leaving the agent to infer the appropriate context from the tool name and description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

B3/5.0
Disambiguation2/5

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.

Naming Consistency3/5

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.

Tool Count2/5

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.

Completeness3/5

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.