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elcoryel

scern-mcp

by elcoryel
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JavaScript
Remote

Scern MCP

scern-mcp MCP server

A product evaluation MCP server for US packaged food. Give your agent a barcode and get back a deterministic, cited verdict. Health scoring, ingredient-level safety, state regulatory flags, FDA recall history, and corporate ownership resolved to the ultimate parent.

Hosted, remote, no install, no auth.

https://api.scern.app/mcp

Paste that URL into Claude or any MCP client that supports Streamable HTTP and start evaluating products.

Live inside Claude

Claude artifacts can call MCP connectors directly. This is Scern powering a live evaluation and a compare-and-swap flow inside a Claude artifact, no integration code written.

Evaluate

Compare and swap

Related MCP server: nutrition-mcp

Why this exists

Agents are starting to buy groceries. When someone tells their agent "order snacks for my kids this week," the agent has to translate a vague request into specific products. What informs that selection? Today the answer is mostly whatever ranked first in search results.

Scern is the evaluation layer for that decision. Same barcode in, same structured verdict out, every time. The agent can compare the options and explain its choice.

Tools

evaluate_product

Evaluate a single product by barcode.

Parameter

Type

Required

Description

identifier

"upc" | "ean" | "barcode"

yes

Barcode identifier type

value

string

yes

The barcode value

jurisdiction

string or string[]

no

Filter regulatory flags. Valid values CA, TX, WV

context

"retail" | "school_meals"

no

Evaluation context, default retail

Returns a full evaluation. Health score with Nutri-Score 2023 letter grade computed from manufacturer panel data, NOVA processing classification, per-ingredient safety review with plain-language explanations, state regulatory flags each carrying the law, effective date, penalty, and source URL, FDA recall history, packaging policy data, and the brand ownership chain resolved to the ultimate parent company.

When the underlying data is insufficient to score honestly, the API returns insufficient_data instead of guessing.

compare_products

Compare 2 to 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.

Parameter

Type

Required

Description

values

string[]

yes

2 to 5 barcode values

identifier

"upc" | "ean" | "barcode"

no

Applied to all values, default upc

jurisdiction

string or string[]

no

Filter regulatory flags. Valid values CA, TX, WV

context

"retail" | "school_meals"

no

Evaluation context, default retail

This is the swap flow. An agent scores the candidates from a search result, proposes the better-scoring alternative, and can explain why. "This one has a Red 40 flag, this alternative scores higher with no flags, want to swap?"

What an evaluation looks like

A real excerpt from evaluating a Pop-Tarts barcode:

{
  "name": "Frosted Strawberry Pop-Tarts",
  "brand": "Pop-Tarts",
  "healthScore": 29,
  "scoreLabel": "Caution",
  "score_basis": "nutrition_derived",
  "healthBreakdown": { "nutriScoreGrade": "e", "novaGroup": 4 },
  "matchStats": { "total": 26, "matched": 26, "matchRate": 1 },
  "regulatory_flags": [
    {
      "jurisdiction": "TX",
      "law_name": "Make Texas Healthy Again Act (SB 25)",
      "status": "warning_label_required",
      "effective_date": "2027-01-01",
      "ingredient_found": "red 40",
      "penalty": "Up to $50,000 per day per violative product",
      "source_url": "https://capitol.texas.gov/BillLookup/History.aspx?LegSess=89R&Bill=SB25"
    }
  ],
  "brandOwnershipChain": {
    "ownershipPath": ["Pop-Tarts", "Kellanova", "Mars, Incorporated"]
  }
}

Every ingredient in the product gets its own reviewed entry. Red 40, Yellow 6, Blue 1, and TBHQ each trigger a Texas SB 25 flag on this product, with the statute cited.

Connecting

Claude (web or desktop). Settings, Connectors, Add custom connector, paste https://api.scern.app/mcp.

Claude Code.

claude mcp add --transport http scern https://api.scern.app/mcp

Any STDIO-only client via mcp-remote:

{
  "mcpServers": {
    "scern": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "https://api.scern.app/mcp"]
    }
  }
}

Plain HTTP. The server speaks Streamable HTTP JSON-RPC. An initialize followed by tools/list works from curl if you want to inspect the schemas directly. There is also a REST endpoint at POST https://api.scern.app/v1/products/evaluate accepting {"identifier": "upc", "value": "..."}.

What's behind it

  • A human-reviewed ingredient dictionary covering roughly 90% of ingredients in US packaged food, each entry carrying a reviewed status and a plain-language explanation

  • Nutri-Score 2023 scoring computed from manufacturer nutrition panel data, with the letter grade served in the payload

  • Regulatory flag tracking for California AB 418, Texas SB 25, Texas SB 314, and West Virginia HB 2354, with jurisdiction filtering and a school meals context

  • FDA recall history via openFDA, checked per evaluation and cached

  • Corporate ownership trees resolved to the ultimate parent, current through recent acquisitions

  • Packaging policy indexing including FCCdb chemical data where available

Determinism is the design constraint. No generative model sits between the database and the verdict. The same barcode returns the same evaluation until the underlying data changes, and every claim in the payload carries its provenance.

Coverage and honesty

Scern covers US packaged food with an emphasis on products marketed to children and families. Coverage is growing. When a product is unknown or its data is too thin to score, the API says so, not_found and insufficient_data are real answers you will see. If you hit coverage gaps that matter to what you are building, open an issue. Gap reports directly prioritize what gets added next.

Feedback

Issues and discussions are open. If you are building an agent that touches food, groceries, nutrition, or shopping, I would genuinely like to hear what works and what breaks.

Built by Cory Lewis. Contact cory@scern.app.

Available Tools

2 tools
compare_productsCompare ProductsA
Read-only

Compare 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.

ParametersJSON Schema
NameRequiredDescriptionDefault
valuesYesArray of 2–5 barcode values to compare
contextNoEvaluation context (default: retail)
identifierNoBarcode identifier type, applied to all valuesupc
jurisdictionNoFilter regulatory flags: CA, TX, or WV

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. Description adds details on output format (evaluations, ranking, best barcode) and error handling (not_found entries), which is helpful beyond 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?

Two sentences, front-loaded with purpose and output, no redundant information. Efficient and clear.

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

Completeness5/5

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

Within the tool's complexity and given no output schema, the description adequately explains inputs, return values, and edge cases (not_found products), making it self-contained.

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

Parameters3/5

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

Input schema has 100% description coverage, so baseline is 3. Description does not add additional semantic value for parameters beyond what the schema already provides.

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

Purpose5/5

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

Clearly states it compares 2-5 products by health score, returns evaluations and ranking, and handles not-found products. Distinguishes from sibling evaluate_product by the multi-product scope and output structure.

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?

Implies use for comparing multiple products but does not explicitly state when to use instead of evaluate_product. Lacks explicit when-to-use or when-not-to-use guidance.

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

evaluate_productEvaluate ProductA
Read-only

Evaluate a product's health score, ingredient safety, corporate ownership, and regulatory flags using the Scern database. Pass a UPC or EAN barcode value.

ParametersJSON Schema
NameRequiredDescriptionDefault
valueYesBarcode value
contextNoEvaluation context (default: retail)
identifierYesBarcode identifier type
jurisdictionNoFilter regulatory flags by jurisdiction. Valid values: CA, TX, WV

TDQS

A3.7/5.0
Behavior3/5

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

Annotations indicate readOnlyHint=true and destructiveHint=false, which align with the description. The description adds database context but lacks details on error handling, rate limits, or behavior with invalid barcodes.

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?

Two sentences, each earning its place: first states purpose, second specifies input. No redundancy, front-loaded with key information.

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

Completeness3/5

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

The tool has 4 params but no output schema; description covers barcode input but omits return structure, pagination, or error scenarios. Adequate for a simple read tool but could be more complete.

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

Parameters3/5

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

Schema coverage is 100%, so schema already documents parameters. The description adds context for identifier (UPC/EAN) but doesn't explain context or jurisdiction parameters, which are self-explanatory from enums.

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

Purpose5/5

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

The description clearly states the tool evaluates a product's health, ingredient safety, ownership, and regulatory flags, using Scern database. It differentiates from sibling compare_products by focusing on single-product evaluation.

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 explains when to use it (evaluate a product via barcode) but does not discuss when not to use or alternatives like compare_products. Usage is clear but no exclusions provided.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 2 tool updatesv1.0.0
    • First observedcompare_products
    • First observedevaluate_product
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TDQS

A3.9/5.0
Disambiguation5/5

The two tools have clearly distinct purposes: one evaluates a single product, the other compares multiple products. There is no ambiguity between them.

Naming Consistency5/5

Both tools follow the consistent verb_noun pattern: evaluate_product and compare_products. The naming is predictable and clear.

Tool Count3/5

With only 2 tools, the surface feels thin even for a focused purpose. While it covers the core functions, additional tools like search or product details would round it out.

Completeness3/5

The tools cover evaluation and comparison, but are missing basic operations like retrieving a product's full details without evaluation or searching by name. The set is minimally functional.

Maintenance

ActivitySlowing
ResponsivenessNo issues

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