feat: Implement Dynamic Agent Orchestrator with MCP

This commit introduces a complete application for a dynamic agent orchestrator.

Key features:
1.  **MCP Server (`app.py`):** Exposes a single generic capability ("dynamic_task_executor"). It manages the application lifecycle.
2.  **Internal Logical Agent (`internal_agent.py`):** Orchestrates tasks by communicating with an internal LLM. It follows a Reason-Act-Observe loop, making decisions to call tools or respond directly.
3.  **Dynamic Tool Management (`tool_manager.py`, `tools.json`):** Low-level tools are defined in an external `tools.json` file and loaded at runtime. The `ToolRegistry` class manages these definitions and maps them to executable Python functions (`tool_functions.py`).
4.  **Mock Internal LLM (`llm_client.py`):** A mock client that simulates an LLM with tool-calling capabilities (OpenAI-like). This allows for testing the agent's logic without a live LLM.
5.  **Configuration (`config.py`, `.env.example`):** Manages settings like LLM endpoints and API keys through environment variables.
6.  **Asynchronous Design:** Core operations are asynchronous.
7.  **Example Tools (`tool_functions.py`):** Includes mock implementations for database queries, REST API calls, and web scraping.
8.  **Testing (`app.py` local tests):** The `app.py` includes an integrated test runner to verify the end-to-end logic from the tool invocation down to the agent and mock tool execution.
9.  **Documentation (`README.md`):** Provides setup, testing, and running instructions. Docstrings and comments are included in the code.

The application is structured for extensibility, allowing new tools to be added by updating the `tools.json` configuration and providing their Python implementations without changing the core agent or server logic.

Author: google-labs-jules[bot]

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