Update all links for cmudrc org transfer

- Jezemba/Aviary → cmudrc/aviary-mcp
- Jezemba/MAS-Aviary → cmudrc/MAS-Aviary
- jezemba.github.io → cmudrc.github.io
- Fix cd directory name after clone (aviary-mcp not Aviary)

Author: Jezemba

README.md

@@ -13,7 +13,7 @@ multi-agent approaches on a real-world engineering optimization problem.
 | Repository | Description |
 |------------|-------------|
 | **MAS-Aviary** (this repo) | Multi-agent LLM framework (client) |
-| [Aviary](https://github.com/Jezemba/Aviary) | MCP server wrapping NASA OpenMDAO/Aviary (backend) |
+| [Aviary](https://github.com/cmudrc/aviary-mcp) | MCP server wrapping NASA OpenMDAO/Aviary (backend) |
 
 ---
 
@@ -88,7 +88,7 @@ structures and three task-handling paradigms:
 ### 1. Clone the repository
 
 ```bash
-git clone https://github.com/Jezemba/MAS-Aviary.git
+git clone https://github.com/cmudrc/MAS-Aviary.git
 cd MAS-Aviary
 ```
 
@@ -179,14 +179,14 @@ Other environment variable overrides:
 
 ### 1. Start the Aviary MCP server
 
-The MCP server ([Aviary repo](https://github.com/Jezemba/Aviary)) provides
+The MCP server ([Aviary repo](https://github.com/cmudrc/aviary-mcp)) provides
 tool-based access to NASA OpenMDAO/Aviary simulations. It must be running
 before launching agents:
 
 ```bash
 # Clone and install the Aviary MCP server (requires a separate conda env)
-git clone https://github.com/Jezemba/Aviary.git
-cd Aviary
+git clone https://github.com/cmudrc/aviary-mcp.git
+cd aviary-mcp
 conda create -n aviary python=3.11 -y
 conda activate aviary
 pip install -r requirements.txt
@@ -199,7 +199,7 @@ The server exposes 9 MCP tools: `get_design_space`, `create_session`,
 `set_aircraft_parameters`, `configure_mission`, `validate_parameters`,
 `run_simulation`, `get_results`, `get_trajectory`, `check_constraints`.
 
-See the [Aviary README](https://github.com/Jezemba/Aviary#readme) for full
+See the [Aviary README](https://github.com/cmudrc/aviary-mcp#readme) for full
 server documentation.
 
 ### 2. Run a single optimization
@@ -261,7 +261,7 @@ pytest -m "slow or gpu" -v --timeout=600
 
 ## Documentation
 
-Full documentation is hosted at **https://jezemba.github.io/MAS-Aviary/**
+Full documentation is hosted at **https://cmudrc.github.io/MAS-Aviary/**
 (auto-deployed on every push to main).
 
 To build locally:

docs/configuration.rst

@@ -44,7 +44,7 @@ connection, and general run parameters.
 
 **MCP section**:
 
-- ``url`` -- Full URL to the `Aviary MCP server <https://github.com/Jezemba/Aviary>`_.
+- ``url`` -- Full URL to the `Aviary MCP server <https://github.com/cmudrc/aviary-mcp>`_.
 - ``transport`` -- Protocol transport. Use ``streamable-http`` for HTTP-based
   MCP.
 - ``timeout`` -- Per-request timeout in seconds.

docs/index.rst

@@ -10,7 +10,7 @@ supports eight distinct coordination strategies formed by combining three
 organizational structures with three operational handlers.
 
 The Aviary MCP server backend is available at
-`github.com/Jezemba/Aviary <https://github.com/Jezemba/Aviary>`_.
+`github.com/cmudrc/aviary-mcp <https://github.com/cmudrc/aviary-mcp>`_.
 
 .. toctree::
    :maxdepth: 2

docs/quickstart.rst

@@ -9,7 +9,7 @@ Prerequisites
 =============
 
 - Python 3.12+ (for MAS-Aviary)
-- The `Aviary MCP server <https://github.com/Jezemba/Aviary>`_ (separate conda env, Python 3.11)
+- The `Aviary MCP server <https://github.com/cmudrc/aviary-mcp>`_ (separate conda env, Python 3.11)
 - NVIDIA GPU with CUDA 12.x (for Qwen3-8B inference)
 
 Installation
@@ -19,7 +19,7 @@ Installation
 
    .. code-block:: bash
 
-      git clone https://github.com/Jezemba/MAS-Aviary.git
+      git clone https://github.com/cmudrc/MAS-Aviary.git
       cd MAS-Aviary
 
 2. **Create and activate a virtual environment**
@@ -39,23 +39,23 @@ Installation
 Starting the Aviary MCP Server
 ==============================
 
-The `Aviary MCP server <https://github.com/Jezemba/Aviary>`_ must be running
+The `Aviary MCP server <https://github.com/cmudrc/aviary-mcp>`_ must be running
 before you launch any optimization. It exposes 9 aircraft design tools that
 agents interact with via the Model Context Protocol.
 
 .. code-block:: bash
 
    # Clone and install in a separate conda environment
-   git clone https://github.com/Jezemba/Aviary.git
-   cd Aviary
+   git clone https://github.com/cmudrc/aviary-mcp.git
+   cd aviary-mcp
    conda create -n aviary python=3.11 -y
    conda activate aviary
    pip install -r requirements.txt
 
    # Start the server (listens on http://localhost:8600/mcp)
    python server/aviary_mcp_server.py
 
-See the `Aviary README <https://github.com/Jezemba/Aviary#readme>`_ for full
+See the `Aviary README <https://github.com/cmudrc/aviary-mcp#readme>`_ for full
 server documentation including design parameters, mission configuration, and
 tool reference.