You must first install uv.

Next, open a terminal window and type uv to confirm that is available. To get the path to uvx, type the following:

which uv

Copy the path returned, and add the following to the JSON file used to configure MCP servers for your LLM app. Be sure to substitute your path to uv as returned above, for the placeholder given:

{
  "mcpServers": {
    "vizro-mcp": {
      "command": "/placeholder-path/uvx",
      "args": [
        "vizro-mcp"
      ]
    }
  }
}

Quick install

Host	Prerequisite	Link
Cursor	uv
VS Code	uv

You must first install Docker.

Next, add the following to the JSON file used to configure MCP servers for your LLM app.

{
  "mcpServers": {
    "vizro-mcp": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "mcp/vizro"
      ]
    }
  }
}

To use local data with Docker

Mount your data directory or directories into the container with the following extended configuration. Replace </absolute/path/to/allowed/dir> (syntax for folders) or </absolute/path/to/data.csv> (syntax for files) with the absolute path to your data on your machine. For consistency, we recommend that the dst path matches the src path.

{
  "mcpServers": {
    "vizro-mcp": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "--mount",
        "type=bind,src=</absolute/path/to/allowed/dir>,dst=</absolute/path/to/allowed/dir>",
        "--mount",
        "type=bind,src=</absolute/path/to/data.csv>,dst=</absolute/path/to/data.csv>",
        "mcp/vizro"
      ]
    }
  }
}

Quick install

Host	Prerequisite	Link	Notes
Cursor	Docker		For local data access, mount your data directory
VS Code	Docker		For local data access, mount your data directory

Users acknowledge and agree that:

Any results, options, data, recommendations, analyses, code, or other information (“Outputs”) generated by any third-party generative AI tools (“GenAI Tools”) may contain some inaccuracies, biases, illegitimate, potentially infringing, or otherwise inappropriate content that may be mistaken, discriminatory, or misleading.

McKinsey & Company:

(i) expressly disclaims the accuracy, adequacy, timeliness, reliability, merchantability, fitness for a particular purpose, non-infringement, safety or completeness of any Outputs,

(ii) shall not be liable for any errors, omissions, or other defects in, delays or interruptions in such Outputs, or for any actions taken in reliance thereon, and

(iii) shall not be liable for any alleged violation or infringement of any right of any third party resulting from the users’ use of the GenAI Tools and the Outputs.

The Outputs shall be verified and validated by the users and shall not be used without human oversight and as a sole basis for making decisions impacting individuals.

Users remain solely responsible for the use of the Output, in particular, the users will need to determine the level of human oversight needed to be given the context and use case, as well as for informing the users’ personnel and other affected users about the nature of the GenAI Output. Users are also fully responsible for their decisions, actions, use of Vizro and Vizro-MCP and compliance with applicable laws, rules, and regulations, including but not limited to confirming that the Outputs do not infringe any third-party rights.

Vizro-MCP is used by generative AI models because large language models (LLMs) represent significant advancements in the AI field. However, as with any powerful tool, there are potential risks associated with connecting to a generative AI model.

We recommend users research and understand the selected model before using Vizro-MCP. We also recommend users to check the MCP server code before using it.

Users are encouraged to treat AI-generated content as supplementary, always apply human judgment, approach with caution, review the relevant disclaimer page, and consider the following:

1. Hallucination and misrepresentation
Generative models can potentially generate information while appearing factual, being entirely fictitious or misleading.

The vendor models might lack real-time knowledge or events beyond its last updates. Vizro-MCP output may vary and you should always verify critical information. It is the user's responsibility to discern the accuracy, consistent, and reliability of the generated content.

2. Unintended and sensitive output
The outputs from these models can be unexpected, inappropriate, or even harmful. Users as human in the loop is an essential part. Users must check and interpret the final output. It is necessary to approach the generated content with caution, especially when shared or applied in various contexts.
3. Data privacy
Your data is sent to model vendors if you connect to LLMs via their APIs. For example, if you connect to the model from OpenAI, your data will be sent to OpenAI via their API. Users should be cautious about sharing or inputting any personal or sensitive information.
4. Bias and fairness
Generative AI can exhibit biases present in their training data. Users need to be aware of and navigate potential biases in generated outputs and be cautious when interpreting the generated content.
5. Malicious use
These models can be exploited for various malicious activities. Users should be cautious about how and where they deploy and access such models.