# Scan For AI Assets

AI Assets discovers components inside your connected environments with scans.\
Each scan runs against a selected environment and populates your AI inventory with newly discovered **Models** or **AI Workflows**. All scans are logged in **Scan History** for traceability.

## Model Scans

Model scans analyze your connected environments to identify [**AI Models**](/ai-asset-management/models.md) integrated into applications. Run Model scans regularly to stay updated on model usage across environments.

**How to run a Model Scan**

1. Navigate to **AI Assets → Models**.
2. Click **Scan Models** in the top right corner.
3. Select the environment(s) you want to scan.
4. Click **Scan** to begin.

Results appear in the **Models view** and include charts, usage breakdowns, and an updated model inventory.

## Workflow Scans

Workflow scans analyze repositories for [**AI Workflows**](/ai-asset-management/ai-workflows.md) and their agents, tools, and MCP servers. Run Workflow scans whenever workflows or agentic architectures evolve.

**How to run a Workflow Scan**

1. Navigate to **AI Assets → AI Workflows**.
2. Click **Scan AI Workflows in the top right corner**.
3. Select the environment(s) you want to scan.
4. Click **Scan** to begin.

Results appear in the **AI Workflows view**, where workflows are visualized as interactive graphs showing architecture, dependencies, and connections.

## MCP Servers Scan

MCP servers scans analyze repositories for  [**MCP servers**](/ai-asset-management/mcp-servers.md) and their tools, prompts, resources, resource templates, and issues. Run MCP servers scans whenever server architectures evolve.

**How to run a MCP Servers Scan**

1. Navigate to **AI Assets → MCP Servers**.
2. Click **Scan MCP Servers** in the top right corner.
3. Select the environment(s) you want to scan.
4. Click **Scan** to begin.

{% hint style="warning" %}
[Some MCP Servers will not be scanned instantly! ](/ai-asset-management/mcp-servers.md#unconnected-remote-servers)

Some servers in a scanned environment require authorization to connect. Once connected successfully, they must be re-scanned manually.
{% endhint %}

## Scan History

The **Scan History** page provides a full audit trail of all scans. Review **Scan History** to ensure full coverage and follow up on failed scans.

\
For each scan, you can see:

* **Environment Name** - the given environment name
* **Environment Type** - the type of environment scanned (currently GitHub or GitLab)
* **Scan Type** - whether the scan targeted **Models, AI Workflows or MCP Server**
* **Created At** - the timestamp of when the scan was initiated
* **Status** - scan state: Finished, In Progress, or Error
* **Progress** - scan completion percentage
* **Assets Count** - the number of AI components discovered

This allows teams to track discovery over time, verify coverage, and repeat scans as needed.

<figure><img src="/files/CS1qWgltpkwk18w3ycmP" alt=""><figcaption><p>Figure 1: Scan History Page</p></figcaption></figure>


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