Product evaluation MCP server for US packaged food. Health scores, ingredient safety, regulatory flags, recall history, corporate ownership.
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Average 4/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes: evaluate_product is for a single product's health evaluation, while compare_products is for ranking multiple products. There is no overlap in their inputs or outputs, making them easy for an agent to distinguish.
Both tools follow a consistent verb_noun pattern using snake_case: evaluate_product and compare_products. The singular/plural noun appropriately reflects whether the action involves one or many products.
With only two tools, the server feels thin and is on the lower end of what is considered well-scoped. However, the two tools do cover the server's core purpose of evaluating and comparing products, so it is not excessive or insufficient for that narrow domain.
For a barcode-based product scoring service, the combination of single-product evaluation and multi-product comparison covers the essential workflows. There are no obvious missing operations since the service is read-only and focuses on analysis rather than CRUD.
compare_productsCompare ProductsACompare 2–5 products by health score. Returns all evaluations, a ranking by health score (descending), and the best-scoring barcode. Products not found are included as { value, status: 'not_found' } and excluded from the ranking.
| Name | Required | Description | Default |
|---|---|---|---|
| values | Yes | Array of 2–5 barcode values to compare | |
| context | No | Evaluation context (default: retail) | |
| identifier | No | Barcode identifier type, applied to all values | upc |
| jurisdiction | No | Filter regulatory flags: CA, TX, or WV |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds valuable behavioral details including the return structure (all evaluations, ranking by health score, best barcode) and edge-case handling for not-found products, which goes 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-loaded with the purpose, and every sentence provides useful information. No filler 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?
With no output schema, the description explains return values clearly (evaluations, ranking, best-scoring barcode) and covers the not-found edge case. Input parameters are fully documented in the schema, making the tool easy to invoke 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?
Schema description coverage is 100% with each parameter well-described. The description adds no additional parameter semantics, so the baseline score of 3 is appropriate.
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 states a specific action ('Compare 2–5 products by health score') with a clear resource and scope. It distinguishes from the sibling tool evaluate_product by focusing on multi-product comparison rather than single evaluation.
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 usage (comparing multiple products) but does not explicitly state when to use it versus evaluate_product, nor provide exclusions or conditions. No alternative tool is named.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
evaluate_productEvaluate ProductAEvaluate a product's health score, ingredient safety, corporate ownership, and regulatory flags using the Scern database. Pass a UPC or EAN barcode value.
| Name | Required | Description | Default |
|---|---|---|---|
| value | Yes | Barcode value | |
| context | No | Evaluation context (default: retail) | |
| identifier | Yes | Barcode identifier type | |
| jurisdiction | No | Filter regulatory flags by jurisdiction. Valid values: CA, TX, WV |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds the database source (Scern) and the categories evaluated (health, ingredients, ownership, regulatory flags), but does not describe return format or any additional behaviors. This adds some context but not rich detail, warranting a 3.
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-loading the purpose and then giving a usage instruction. No unnecessary words, every sentence adds value.
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?
With no output schema, the description should convey what the tool returns; it lists the evaluation areas (health score, ingredient safety, corporate ownership, regulatory flags), which gives an adequate sense of output. However, it does not mention the optional context/jurisdiction parameters or how results are structured, so it is not fully comprehensive but is sufficient for a moderate-complexity, read-only tool.
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 input schema provides descriptions for all 4 parameters (100% coverage), so the baseline is 3. The description's phrase 'Pass a UPC or EAN barcode value' merely restates the schema's identifier and value descriptions without adding syntax or format details beyond what's already present.
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 opens with 'Evaluate a product's health score, ingredient safety, corporate ownership, and regulatory flags' which clearly identifies the action and resource. It also names the database (Scern) and specifies the input (UPC/EAN barcode), making it distinct from the sibling compare_products.
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 gives the input requirement ('Pass a UPC or EAN barcode value') which implies usage for single-product evaluation. However, it does not explicitly compare against the sibling compare_products or state when not to use it, so usage context is only implied.
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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