# Server configuration

If you have not done it, first [install WedoLow MCP Server (Beta) package](/documentation/wedolow-mcp-server/server-installation.md).

{% hint style="warning" %}
If you installed and configured the WedoLow MCP server using WedoLow VSCode extension, your MCP Server is already configured for VSCode (GitHub Copilot).

If you want to use it with another AI agent, run VSCode command `MCP: List servers...`, select `wedolow-mcp-server` and then `Show Configuration`. This will show you the configuration to use for any other AI agent, then you just need to adapt the commands in the instructions below.
{% endhint %}

## General

The WedoLow MCP Server is an stdio MCP server, which is used as a subprocess by the AI agent that you will be using. To do that, you must know the path of the WedoLow MCP Server executable. If you installed the WedoLow MCP Server following this documentation, then the path is:

{% tabs %}
{% tab title="Linux" %}

```bash
~/.venv-wedolow/bin/wedolow-mcp-server
```

{% endtab %}

{% tab title="Windows" %}

```powershell
${env:USERPROFILE}\.venv-wedolow\Scripts\wedolow-mcp-server.exe
```

{% endtab %}
{% endtabs %}

If not, adapt what follows to the place where the MCP is installed.

{% hint style="info" %}
You may also add the executable directory to your user's path to access it with its base name.
{% endhint %}

## VSCode (GitHub Copilot)

First, install [GitHub Copilot extension](https://marketplace.visualstudio.com/items?itemName=GitHub.copilot).

To add WedoLow MCP Server to GitHub Copilot in VSCode, run VSCode command (CTRL+MAJ+P):

*MCP: Add server...* → *Command (stdio)*

Fill the following informations when asked:

* *Command*: Path to wedolow-mcp-server executable (you may use `${userHome}` as a variable)
* *Server ID*: wedolow-mcp-server
* *Where*: Global

WedoLow MCP Server is now available for all your projects.

{% hint style="info" %}
Instead of *Global*, you may select *Workspace* if you want to activate the MCP server only for the current workspace. This will create a `mcp.json` file in you project.
{% endhint %}

Try running the MCP Server by running VSCode command (CTRL+MAJ+P):

*MCP: List servers...* → *wedolow-mcp-server* → *Start Server*

You may see the WedoLow MCP Server output by going to:

*MCP: List servers...* → *wedolow-mcp-server* → *Show Output*

This could help troubleshooting if needed.

## CLion (Junie)

In CLion, enable MCP servers:

*Settings* → *Tools* → *MCP Server* → *Enable MCP Server*

Then, add WedoLow MCP Server:

*Settings* → *Tools* → *AI Assistant* → *Model Context Protocol (MCP)* → *Add Server*

Fill the following informations when asked:

* *Name*: wedolow-mcp-server
* Command: Path to wedolow-mcp-server executable

## Gemini CLI

Adding WedoLow MCP Server to Gemini CLI can be done with this one-liner in your terminal:

{% tabs %}
{% tab title="Linux" %}

```sh
gemini mcp add --trust --timeout=3600000 -s user wedolow-mcp-server "${HOME}/.venv-wedolow/bin/wedolow-mcp-server"
```

{% endtab %}

{% tab title="Windows" %}

```powershell
gemini mcp add --trust --timeout=3600000 -s user wedolow-mcp-server "${env:USERPROFILE}\.venv-wedolow\Scripts\wedolow-mcp-server.exe"
```

{% endtab %}
{% endtabs %}

## Claude Code

Adding WedoLow MCP Server to Claude Code can be done with this one-liner in your terminal:

{% tabs %}
{% tab title="Linux" %}

```sh
claude mcp add --scope user wedolow-mcp-server -t stdio "${HOME}/.venv-wedolow/bin/wedolow-mcp-server"
```

{% endtab %}

{% tab title="Windows" %}

```powershell
claude mcp add --scope user wedolow-mcp-server -t stdio "${env:USERPROFILE}\.venv-wedolow\Scripts\wedolow-mcp-server.exe"
```

{% endtab %}
{% endtabs %}

## Other AI agents

Though we could not test other AI agents, most of them support Model Context Protocol (MCP). You may find a compatibility list [here](https://modelcontextprotocol.io/clients).

Search for "MCP" in your AI agent public documentation to find how to register an MCP server.

Feel free to test any other AI agent and tell us more about your experience!


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.wedolow.com/documentation/wedolow-mcp-server/server-configuration.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.