Skip to main content
Glama

Recall Kitchen

by io.github.adamdecaf

search_product_recalls_by_upc

Read-onlyIdempotent

Search recalls by UPC/EAN or a barcode image URL/data URI/MCP image content. Matches extracted recall UPCs first. Returns found=false when the UPC is unknown.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
upcNoUPC or EAN barcode digits
urlNoalias for image_url
limitNonumber of recalls to return, 1-100, default 3
offsetNonumber of recalls to skip
barcodeNoalias for upc
image_urlNooptional HTTPS URL or data:image/...;base64 URI of a barcode image

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
upcYes
hintNo
foundYes
matchNo
offsetYes
productNo
recallsYes
confidenceNo
nextOffsetNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already convey read-only, idempotent, and non-destructive behavior. The description adds genuinely useful behavior beyond that: extracted recall UPCs are matched first, and an unknown UPC yields found=false rather than an error. This is non-obvious and not expressed elsewhere.

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 three short sentences with no wasted words. It front-loads the search verb and resource, then adds the most important matching and not-found behaviors. It avoids repeating schema details like limit or offset.

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?

Given the annotations, full input schema coverage, and presence of an output schema, the description is largely complete: it covers input modes, matching behavior, and not-found semantics. The only real gap is that 'MCP image content' is referenced but no corresponding parameter explicitly represents it in the schema.

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 the baseline is 3 even without additional parameter explanation. The description reinforces that UPC/EAN and image URL/data URI inputs are supported and mentions 'MCP image content,' but it does not substantially clarify aliases or formats beyond what the schema already documents.

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 clearly states the tool searches recalls by UPC/EAN or barcode image input, which identifies the verb, resource, and input modes. It does not explicitly distinguish this from sibling tools such as search_product_recalls_from_image or search_recalls_by_identifier, but the UPC/EAN focus makes the primary purpose unambiguous.

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 the tool should be used when the caller has UPC/EAN digits or a barcode image, and it specifies that unknown UPCs return found=false. However, it gives no explicit guidance about when to prefer this tool over overlapping siblings, especially search_product_recalls_from_image, which also appears image-capable.

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

All Recall Kitchen toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.1/5.0
Disambiguation4/5

Most tools have clear, distinct resource-action boundaries (inventory vs. watch patterns vs. notifications vs. search). The search tools overlap somewhat—especially search_product_recalls_by_upc, search_product_recalls_from_image, and search_recalls_by_identifier—but their input types and intent are described well enough to avoid major confusion.

Naming Consistency4/5

The overwhelming majority follow a predictable verb_noun snake_case pattern (add_inventory_product, list_watch_patterns, revoke_api_key). Minor deviations like signup, list_inventory, and search_recalls_by_identifier (instead of search_product_recalls_by_identifier) keep it from being perfectly consistent.

Tool Count4/5

With 19 tools, the server is slightly on the heavier side, but the count is justified by covering several distinct areas: account/key management, inventory, watch patterns, recall search, and notifications. Each tool appears to serve a real purpose, though a few search variants could arguably be consolidated.

Completeness4/5

The set covers the core lifecycle well: signup, key management, inventory and watch pattern add/list/remove, multiple recall search paths, recall detail lookup, and notification read state. Minor gaps exist, such as no update operation for inventory items or watch patterns and no bulk notification actions, but agents can work around these.

Resources