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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.

Motivation and Context

How Has This Been Tested?

Breaking Changes

Types of changes

  • Bug fix (non-breaking change which fixes an issue)
  • New feature (non-breaking change which adds functionality)
  • Breaking change (fix or feature that would cause existing functionality to change)
  • Documentation update

Checklist

  • I have read the MCP Documentation
  • My code follows the repository's style guidelines
  • New and existing tests pass locally
  • I have added appropriate error handling
  • I have added or updated documentation as needed

Additional context

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.
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