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Glama

Recall Kitchen

by io.github.adamdecaf

search_product_recalls_from_image

Read-onlyIdempotent

Identify products in a public HTTPS image URL, data:image/...;base64 URI, or MCP image content part. Local files are not supported. Each product includes match (upc, model, text, category) and confidence. Category matches omit recalls unless include_category_matches=true. Requires an API key or x402; not included in the free anonymous per-IP quota.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNoalias for image_url
limitNonumber of recalls to return per product, 1-100, default 3
image_urlNoHTTPS URL or data:image/...;base64 URI of a photo containing products. Local file paths are not supported.
include_category_matchesNoif true, attach keyword recalls for generic items such as cups; default false

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
recallsYes
productsYes

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the read-only/idempotent annotations, the description discloses important behavior: accepted input formats, local-file rejection, per-product match and confidence output, the include_category_matches behavior, and authentication/quota requirements. This gives the agent a clear picture of constraints and expectations without contradicting 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?

The description is compact and well-organized, leading with the core input modes, then constraints, output shape, category behavior, and auth requirements. Every sentence earns its place and there is no redundant repetition of schema details.

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?

For a read-only image search tool with an output schema, the description covers all essential calling context: supported inputs, unsupported local files, the auth/quota condition, and the special category-match behavior. No critical operational detail is missing.

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

Parameters4/5

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

The schema already documents all parameters at 100% coverage, so the baseline is 3. The description adds meaningful extra semantics by noting MCP image content parts as a supported input and explaining that category matches omit recalls unless include_category_matches=true.

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's purpose: identify products in an image supplied via HTTPS URL, base64 data URI, or MCP image content part. This verb+resource framing and the explicit input-mode list differentiate it from the sibling text/UPC/identifier search tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context for when to use the tool (when you have an image reference) and explicitly excludes local files. It also notes the API key/x402 requirement and the free-quota exclusion. It does not name sibling tools as alternatives, but the image-focused wording makes the intended use unambiguous.

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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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