mcp server
DeepRecall - Product Safety Intelligence
Search 120,000+ recalled products from 8 global safety agencies using AI similarity.
Description as published by the maintainer. Source
- version 1.0.0
- slowing
- retrieval
- memory and context
slowing — Most recent push to the repository was 2026-01-27. Dashed tags are derived by ZBS Index from the published description, not stated by the maintainer.
What this server can do
2 functions, named and described by the server itself. Parameter names are shown because they say more about what a function does than its name usually does.
get_data_sources- 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
search_recalls(top_k, api_key, image_urls, model_name, input_weights, content_description, filter_by_data_sources)- 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 )
Last successful function declaration observed on . Source: https://mcp.deeprecall.io/mcp. We list what the server declared; we do not call any of these functions.
Endpoint status observed on . Source: https://mcp.deeprecall.io/mcp.
Signals
These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.
| Signal | Value | What it measures | Window | Observed | Source |
|---|---|---|---|---|---|
| GitHub stars | 0 | Number of GitHub accounts that bookmarked this repository since it was created. It is a bookmark count, not installs, not active users and not quality. | cumulative, all time | GitHub | |
| Last commit | 2026-01-27 | Date of the most recent push to any branch. This is the strongest cheap indicator of whether the project is still maintained. | point in time | GitHub | |
| Open issues | 0 | Open issues plus open pull requests, as GitHub counts them together. A high number can mean an active project or an abandoned one. | as of fetch | GitHub | |
| Latest published version | 1.0.0 | Latest version string the maintainer published to the registry. | as of fetch | Model Context Protocol | |
| Registry record last updated | 2026-01-27 | When the registry record was last updated by its maintainer. | point in time | Model Context Protocol | |
| License | Apache-2.0 | Licence GitHub detected in the repository. Detection can be wrong; the LICENSE file is authoritative. | as of fetch | GitHub | |
| First listed in the MCP Registry | 2026-01-27 | Date this server was first published to the official MCP Registry. Not a usage or quality measure. | point in time | Model Context Protocol | |
| repository status | active | The repository exists on GitHub and is not archived. This says nothing about how recently it was worked on. | as of fetch | GitHub | |
| mcp tools declared | 2 tools | Number of functions the server itself declared when asked to list them. This is what the server offers an agent, not a measure of how well any of them work. | as of probe | mcp.deeprecall.io | |
| mcp endpoint status | ok | The server listed 2 functions when asked. | as of probe | mcp.deeprecall.io |
Where to get it
This record as data
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