A
licenseNot graded
qualityC
maintenanceAccess US consumer-product safety recalls from the CPSC, free and without authentication.
16
MIT
DeepRecall - Product Safety Intelligence
Server Details
Search 120,000+ recalled products from 8 global safety agencies using AI similarity.
If you are the author of this connector, you can claim ownership with GitHub, an HTTP challenge, or a DNS record. Claimed connector authors can inspect health checks, view analytics, and manage their listing.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- DeepRecall-io/deeprecall-mcp
- GitHub Stars
- 0
- Server Listing
- deeprecall-mcp
Available Tools
2 toolsget_data_sourcesAInspect
Get information about available recall data sources.
Returns a list of all supported regulatory agencies and their coverage.
This is a free call that does not consume API credits.
Returns:
Dictionary with data sources and their descriptions
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
A4.4/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses a key trait: the call is free and does not consume API credits. It also states the return format ('Dictionary with data sources and their descriptions'). While it does not mention side effects or limitations, for a read-only informational endpoint with zero parameters this is adequate and adds value beyond the schema.
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 concise—three short sentences—that front-load the main purpose in the first sentence. The 'Returns:' section is formatted for quick scanning. Every sentence earns its place without redundancy or fluff.
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 zero-parameter tool with a simple output, the description is largely complete: it states what the tool does, what it returns (list of agencies and coverage), and that it is free. It could clarify what 'coverage' means or the exact structure of the returned dictionaries, but for the tool's simplicity this is sufficient.
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 input schema has zero parameters, so the description has no parameter meanings to explain. Per the rubric, a tool with zero parameters gets a baseline of 4. The description does not detract from this and appropriately focuses on output rather than inputs.
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: 'Get information about available recall data sources.' It specifies the scope ('all supported regulatory agencies and their coverage'), which distinguishes it from the sibling tool 'search_recalls' that searches for recall records. The verb 'get' and resource 'data sources' are specific.
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 provides clear context for when to use the tool: when you need a list of data sources or regulatory agencies. It also notes 'This is a free call that does not consume API credits,' implying it can be used without cost concern. However, it does not explicitly mention when not to use it or compare it to alternatives, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_recallsAInspect
Search for recalled products similar to your query.
This tool searches DeepRecall's global product safety database using AI-powered
multimodal matching. Provide a text description and/or product images to find
similar recalled products.
Use Cases:
- Pre-purchase safety checks: Before buying, verify if similar products were recalled
- Supplier vetting: Check if a supplier's products have safety issues
- Marketplace compliance: Verify products against recall databases
- Consumer protection: Identify potentially hazardous products
Data Sources:
- us_cpsc: US Consumer Product Safety Commission
- us_fda: US Food and Drug Administration
- safety_gate: EU Safety Gate (Europe)
- uk_opss: UK Office for Product Safety & Standards
- canada_recalls: Health Canada Recalls
- oecd: OECD GlobalRecalls portal
- rappel_conso: French Consumer Recalls
- accc_recalls: Australian Competition and Consumer Commission
Cost: 1 API credit per search
Args:
content_description: Text description of the product (e.g., "children's toy with small parts")
image_urls: List of product image URLs for visual matching (1-10 images)
filter_by_data_sources: Limit search to specific agencies (optional)
top_k: Number of results (1-100, default: 10)
model_name: Fusion model - fuse_max (recommended), fuse_flex, or fuse
input_weights: Weights for [text, images], must sum to 1.0
api_key: Your DeepRecall API key (optional if provided via X-API-Key header)
Returns:
Search results with matched recalls, scores, and product details
Example:
search_recalls(
content_description="baby crib with drop-side rails",
top_k=5
)
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| top_k | No | ||
| api_key | No | ||
| image_urls | No | ||
| model_name | No | fuse_max | |
| input_weights | No | ||
| content_description | No | ||
| filter_by_data_sources | No |
TDQS
A4.4/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively discloses cost ('1 API credit per search'), data sources, multimodal matching, and the return type. It does not mention rate limits, error behavior, or explicit read-only status, but the 'search' verb implies a safe read operation and the cost note adds useful context beyond what the schema provides.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (summary, use cases, data sources, cost, args, returns, example) and front-loaded purpose. It is somewhat long, but each section earns its place given the tool's complexity. The data source list is extensive, yet it directly supports the filter_by_data_sources parameter. No redundant information.
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 high complexity (7 parameters, no annotations, no output schema), the description provides strong coverage: purpose, use cases, data sources, cost, parameter constraints, an example, and a high-level returns statement. It omits detailed return fields or error handling, but the example and parameter details make the tool functionally understandable. A bit more output detail would make it fully complete, but it is very good as-is.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It does so thoroughly: every parameter is explained with types, constraints, defaults, and examples. For instance, image_urls is limited to 1-10 images, top_k to 1-100 with default 10, model_name lists valid values, and input_weights must sum to 1.0. This goes far beyond the bare schema titles and provides actionable semantics.
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 opens with a clear, specific verb and resource: 'Search for recalled products similar to your query.' It further clarifies the tool's scope via 'DeepRecall's global product safety database' and 'AI-powered multimodal matching.' This clearly distinguishes it from the sibling tool get_data_sources, which is about listing data sources rather than searching.
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 provides explicit use cases (pre-purchase safety checks, supplier vetting, marketplace compliance, consumer protection) that tell the agent when to use this tool. It does not explicitly contrast with sibling tools or state when not to use it, but the scenarios are concrete and the data sources list informs filtering. This is clear guidance, though not as explicit as naming alternatives.
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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Discussions
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TDQS
A4.4/5.0
Disambiguation5/5
get_data_sources provides metadata about available regulatory agencies, while search_recalls performs the actual product recall search. These are clearly distinct purposes with no functional overlap, making tool selection unambiguous.
Naming Consistency5/5
Both tools follow a consistent verb_noun pattern with snake_case (get_data_sources, search_recalls). The naming is predictable and matches the server's search-oriented purpose.
Tool Count3/5
With only two tools, the server feels minimal. However, for a specialized search service, a metadata tool and a search tool may be sufficient. Still, the count is on the thin side, potentially limiting discoverability of other useful capabilities.
Completeness4/5
The core workflow of discovering available data sources and searching for recalls is covered. A minor gap is the lack of a dedicated tool to retrieve detailed information for a specific recall ID, but the search results include sufficient product details, so agents can work around this.