Use "Open edX" trademark properly in the documentation

docs/decisions/0003-xapi-event-tracking.rst

@@ -16,7 +16,7 @@ Different AI workflows exhibit different interaction patterns (one-shot vs. conv
 Decision
 ********
 
-AI workflow executions will emit xAPI-compliant events using Open edX’s existing eventtracking and event-routing-backends infrastructure.
+AI workflow executions will emit xAPI-compliant events using the Open edX platform’s existing eventtracking and event-routing-backends infrastructure.
 
 * Events are emitted via `eventtracking.tracker.emit()` and transformed to xAPI by ERB when Aspects is installed.
 * Direct dependencies on `eventtracking` and `event-routing-backends` are used (no `edxapp_wrapper` abstraction).

docs/how-tos/mcp_example_server.rst

@@ -131,7 +131,7 @@ Create a file named ``run_server.py``:
 Step 3: Expose the Server with ngrok
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Since MCP clients (including OpenEdx AI Extensions) need to access the server via a public URL, you must expose your local server using ngrok:
+Since MCP clients (including Open edX AI Extensions) need to access the server via a public URL, you must expose your local server using ngrok:
 
 .. code-block:: bash
 
@@ -157,7 +157,7 @@ Create a file named ``client_example.py`` to test your server:
     Example MCP client using LiteLLM
 
     This demonstrates how to connect to an MCP server and use its tools
-    from a language model. This is similar to how OpenEdx AI Extensions
+    from a language model. This is similar to how Open edX AI Extensions
     will interact with your MCP servers.
     """
     import asyncio
@@ -224,10 +224,10 @@ Follow these steps to test the complete MCP workflow:
 
        python client_example.py
 
-Testing with OpenEdx AI Extensions
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Testing with Open edX AI Extensions
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-To integrate this example server with OpenEdx AI Extensions:
+To integrate this example server with Open edX AI Extensions:
 
 1. **Configure the MCP server** in your Django settings:
 
@@ -317,7 +317,7 @@ Remember:
 Further Reading
 ---------------
 
-- :doc:`mcp_integration` - Main MCP integration guide for OpenEdx AI Extensions
+- :doc:`mcp_integration` - Main MCP integration guide for Open edX AI Extensions
 - `FastMCP Documentation <https://github.com/jlowin/fastmcp>`_ - Complete FastMCP framework documentation
 - `MCP Specification <https://modelcontextprotocol.io/>`_ - Official Model Context Protocol specification
 

docs/how-tos/mcp_integration.rst

@@ -2,7 +2,7 @@ Model Context Protocol (MCP) Integration
 =========================================
 
 .. warning::
-   **Important: OpenEdx AI Extensions acts as an MCP CLIENT only**
+   **Important: Open edX AI Extensions acts as an MCP CLIENT only**
 
    This application **does not run or host MCP servers**. It only connects to external MCP servers as a client.
 
@@ -13,13 +13,13 @@ Model Context Protocol (MCP) Integration
 Overview
 --------
 
-The Model Context Protocol (MCP) is an open standard that enables AI assistants to securely interact with external tools and data sources. OpenEdx AI Extensions integrates with MCP by acting as a **client** that can connect to external MCP servers, allowing your AI workflows to leverage custom tools and capabilities.
+The Model Context Protocol (MCP) is an open standard that enables AI assistants to securely interact with external tools and data sources. Open edX AI Extensions integrates with MCP by acting as a **client** that can connect to external MCP servers, allowing your AI workflows to leverage custom tools and capabilities.
 
 Key Concepts
 ~~~~~~~~~~~~
 
 - **MCP Server**: An external service that exposes tools and resources via the MCP protocol. You must deploy and manage these servers independently.
-- **MCP Client**: OpenEdx AI Extensions acts as a client, connecting to your MCP servers and making their tools available to AI workflows.
+- **MCP Client**: Open edX AI Extensions acts as a client, connecting to your MCP servers and making their tools available to AI workflows.
 - **MCP Tools**: Functions exposed by MCP servers that the AI model can call to perform specific operations (e.g., data retrieval, computations, integrations).
 
 Architecture
@@ -28,7 +28,7 @@ Architecture
 .. code-block:: text
 
     ┌────────────────────────────────────────────┐
-    │   OpenEdX AI Extensions                    │
+    │   Open edX AI Extensions                   │
     │   (MCP Client / Orchestrator)              │
     │                                            │
     │   ┌──────────────────────┐                 │

docs/index.rst

@@ -6,7 +6,7 @@
 openedx_ai_extensions
 =====================
 
-A experimental plugin for Open edX designed to explore AI extensibility
+A experimental plugin for the Open edX platform designed to explore AI extensibility
 
 Contents: