request_dataFREE, no payment. Describe in your own words the data you are trying to buy — anything, not just food — and get back whether we operate a service that supplies it, with the MCP endpoint if so. Every request is catalogued so repeatedly-requested data gets built. Nothing identifying is stored, only the words of the request.
| Name | Required | Description | Default |
|---|---|---|---|
| need | Yes | What you are looking for. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that the request is free, catalogued, influences future data builds, and that nothing identifying is stored. This is meaningful behavioral context beyond the schema.
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 compact and front-loaded, with the free nature and core behavior appearing first. It includes only relevant information, though 'FREE, no payment' is slightly redundant with the title.
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 single-parameter tool without an output schema, the description adequately covers what input is needed, what the user will receive, and important side effects like cataloguing and privacy. The exact response shape is unspecified but not critical here.
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 already documents 'need' with 100% coverage, so baseline is 3. The description adds value by explaining the parameter expects natural-language description ('in your own words'), has broad scope ('anything, not just food'), and stores only the words of the request.
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 and resource: describe the data you want and receive whether the service exists, with the MCP endpoint if so. It also distinguishes itself from food-specific siblings by saying 'anything, not just food'.
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 broad applicability and hints that food is not required, but it does not explicitly say when to prefer this tool over demand_report or food_by_barcode. Guidance is present only indirectly.
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.
food_by_barcode is clearly distinct as the only direct food-data tool. demand_report and request_data both deal with data-demand signals, but one is an aggregate read-only report and the other is a direct query/submission, so the descriptions keep them mostly separable.
Names are readable and consistently lowercase snake_case, but they follow different patterns: noun_noun (demand_report), noun_by_key (food_by_barcode), and verb_noun (request_data). The semantic similarity of 'demand' and 'request' also blurs the two meta-tools.
With three tools, the server is within the ideal compact range and each tool has a distinct job: one paid food lookup, one demand aggregator, and one data-request channel. Nothing feels redundant or excessive for the marketplace concept.
The barcode lookup covers the eatability fields well, and request_data/demand_report close part of the loop for missing data. However, there is no search by product name or category, and the only actual food-facts operation is a single barcode lookup, so broader Food Facts workflows have notable gaps.