nutrition
Server Details
Nutrition MCP — wraps Open Food Facts API (free, no auth)
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- pipeworx-io/mcp-nutrition
- GitHub Stars
- 0
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Tool Definition Quality
Average 3.4/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes: get_product retrieves detailed nutrition data for a specific barcode, while search_products finds products by general criteria and returns summary information. There is no overlap or ambiguity between them.
Both tools follow a consistent verb_noun pattern (get_product, search_products) with clear, descriptive names that align with their functions. The naming is uniform and predictable.
With only two tools, the server feels under-scoped for a nutrition domain. It lacks essential operations like creating, updating, or deleting product data, and does not cover broader nutritional analysis or meal planning, which are common in this domain.
The tool surface is severely incomplete for a nutrition server. It only supports retrieval and search, missing core functionalities such as adding custom foods, calculating nutritional totals, or managing dietary profiles, which are typical in nutrition applications.
Available Tools
2 toolsget_productAInspect
Get full nutrition details for a food product by its barcode (EAN/UPC).
| Name | Required | Description | Default |
|---|---|---|---|
| barcode | Yes | Product barcode (EAN-13 or UPC-A) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states this is a read operation ('Get'), but doesn't disclose behavioral traits like authentication requirements, rate limits, error conditions, or what happens with invalid barcodes. For a tool with no annotation coverage, this is a significant gap.
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 a single, efficient sentence with zero wasted words. It's front-loaded with the core purpose and includes all necessary information 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 simple lookup tool with one parameter and no output schema, the description covers the basic purpose adequately. However, without annotations or output schema, it lacks information about return format, error handling, and other behavioral aspects that would be helpful for an agent.
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 100%, so the schema already fully documents the single 'barcode' parameter with its format requirements. The description adds no additional parameter semantics beyond what's in the schema, meeting the baseline for high schema coverage.
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 clearly states the verb 'Get' and the resource 'full nutrition details for a food product', specifying the exact scope of data retrieved. It distinguishes from the sibling 'search_products' by focusing on lookup by barcode rather than search functionality.
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 this tool: 'by its barcode (EAN/UPC)', providing clear context for usage. However, it doesn't explicitly mention when not to use it or name the alternative 'search_products' tool, though the distinction is implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_productsBInspect
Search for food products by name, brand, or keyword. Returns product name, brand, Nutri-Score, and key nutrition facts.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of results to return (1-20, default 5) | |
| query | Yes | Search query (product name, brand, or ingredient) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions what the tool returns but doesn't describe important behaviors like pagination, rate limits, authentication requirements, error conditions, or whether this is a read-only operation. The description provides basic functional information but lacks operational context.
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 perfectly concise - a single sentence that efficiently communicates the tool's function and return values. Every word earns its place with no wasted text, and the information is front-loaded appropriately.
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 search tool with 2 parameters and no output schema, the description provides basic functional information but lacks important context. Without annotations or output schema, it should ideally describe the return format more thoroughly (structure of results, pagination behavior) and operational constraints. The description is adequate but has clear gaps.
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 100%, so the schema already documents both parameters thoroughly. The description mentions searching 'by name, brand, or keyword' which aligns with the query parameter, but adds no additional semantic context beyond what's in the schema. This meets the baseline for high schema coverage.
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 clearly states the tool's purpose with specific verbs ('search for food products') and resources ('food products'), and mentions what it returns. However, it doesn't explicitly distinguish this search tool from its sibling 'get_product' tool, which might be for retrieving specific products by ID rather than searching.
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 provides no guidance on when to use this tool versus its sibling 'get_product'. It mentions what the tool does but doesn't specify use cases, prerequisites, or alternatives. This leaves the agent without clear direction on tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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