Skip to main content
Glama

Recall Kitchen

by io.github.adamdecaf

search_recalls_by_identifier

Read-onlyIdempotent

Search recalls by extracted UPC, lot code, model number, product name, or VIN. VIN is decoded locally to year and make and matched against NHTSA campaigns (not a live NHTSA VIN API). Multiple identifiers are AND-matched on the same product.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
upcNoproduct UPC or EAN as stored on the recall
vinNo17-character vehicle identification number. Decoded locally to year and make, then matched against NHTSA campaigns. Does not call NHTSA live.
limitNonumber of recalls to return
offsetNonumber of recalls to skip
lot_codeNolot or batch code as stored on the recall
model_numberNomodel number as stored on the recall
product_nameNoextracted product name (exact match)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
offsetYes
recallsYes
nextOffsetNo

Schema Changelog

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

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already establish that the operation is read-only and non-destructive. The description adds meaningful behavioral context beyond that: local VIN decoding, no live NHTSA VIN API call, and AND-matching semantics across identifiers on the same product.

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 efficient sentences convey the core behavior, the VIN limitation, and matching semantics with no filler. The most important scope information is front-loaded.

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?

For a read-only search tool with a full output schema and complete parameter documentation, the description is nearly sufficient. It covers the key behavioral constraints and identifier types, though it does not state what happens when no identifiers are provided; annotations and schema mitigate this gap.

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?

Schema description coverage is 100%, so the baseline is 3. The description adds value by clarifying that multiple identifiers are AND-matched on the same product, which is combination semantics not obvious from individual parameter descriptions. This modest addition justifies a 4.

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 names a specific verb and resource: searching recalls by one or more extracted identifiers (UPC, lot code, model number, product name, VIN). It further distinguishes itself from sibling tools by explaining the VIN is decoded locally and matched against NHTSA campaigns rather than called live.

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 usage context: use when searching recalls by extracted identifiers, especially when VIN is involved. However, it does not explicitly say when to choose this tool over siblings like search_product_recalls_by_upc or search_product_recalls_from_image, nor does it state exclusions.

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