Refactor Axiom 2.0 documentation by adding web search capability using Tavily MCP, and updating MCP server configuration details in chainlit.md and README.md

.github/README.md

@@ -5,27 +5,27 @@
 Axiom 2.0 is an **advanced AI Agent** built to assist developers in navigating the complex landscape of modern AI and software development. Leveraging the **OpenAI Agents SDK**, **Model Context Protocol (MCP)**, Chainlit, and Gemini models, Axiom 2.0 specializes in generating accurate, production-ready code and providing expert guidance across areas like agent architectures, RAG systems, authentication, vector databases, and full-stack development.
 
 Axiom 2.0 is an upgrade of Axiom 1.0, it has the following more features:
-1. **Mandatory Planning & Documentation:** Axiom 2.0 uses a structured planning phase before generating code and projects. This ensures higher accuracy and adherence to best practices.
-2. **Production-Ready Code Focus:** Axiom 2.0's primary goal is to generate production-quality, modular, efficient, scalable, and readable code, including end-to-end projects and full-stack applications.
-3. **Vast Library Database:** It has access to a vast library database (**2000+** libraries, frameworks, and tools) unlike it's predecessor, Axiom 1.0 which had only 20 libraries documentation.
+1. **Production-Ready Code Focus:** Axiom 2.0's primary goal is to generate production-quality, modular, efficient, scalable, and readable code, including end-to-end projects and full-stack applications.
+2. **Vast Library Database:** It has access to a vast library database (**2000+** libraries, frameworks, and tools) unlike it's predecessor, Axiom 1.0 which had only 20 libraries documentation.
 
 ## Features
 
 *   🤖 **Interactive Chat Interface:** Built with Chainlit for a smooth conversational experience.
 *   📚 **Advanced Documentation Access:** Utilizes a dedicated MCP server to retrieve and apply technical information from various sources, ensuring code and explanations are accurate and up-to-date.
-*   🧠 **Strategic Planning:** Employs a **Sequential Thinking MCP** tool to break down complex requests, plan code architecture, and structure its responses effectively.
 *   🧪 **Two Modes:** 
     1. **Agent Mode:** Ideal for complex tasks like end-to-end projects and full-stack development.
     2. **Assistant Mode:** Suited for quick information retrieval and answering questions.
+*   🧠 **Web Search:** It uses Tavily MCP server to search the web and extract content from URLs.
 *   📦 **Containerized Deployment:** Includes a `Dockerfile` for easy deployment to platforms like Hugging Face Spaces or Docker environments.
 
+
 ## How it Works
 
 Axiom 2.0 operates using the OpenAI Agents SDK. MCP servers are integrated as specialized tools:
 
-1.  **Configuration (`mcp.json`):** The `mcp.json` file in the project root defines the MCP servers (like the Documentation and Sequential Thinking servers).
+1.  **Configuration (`mcp.json`):** The `mcp.json` file in the project root defines the MCP servers (like the Context7 and Tavily servers).
 2.  **Agent Initialization:** When the Chainlit application starts, it reads `mcp.json`, initializes the specified `MCPServerStdio` instances, and attempts to start their underlying processes.
-3.  **Response Integration:** The MCP server performs its task (e.g., searching documentation, generating a thinking process) and sends the result back to the agent via the SDK. The agent then uses this information to formulate its final response to the user.
+3.  **Response Integration:** The MCP server performs its task (e.g., searching documentation, searching web) and sends the result back to the agent via the SDK. The agent then uses this information to formulate its final response to the user.
 
 ## Prerequisites
 
@@ -87,7 +87,7 @@ The `-w` flag enables auto-reloading during development. For production, omit `-
     ```
 
 2.  **Run the Docker container:**
-    Map the container's port 7860 (where Chainlit runs) to a port on your host machine (e.g., 7860). Pass necessary environment variables.
+    Map the container's port 7860 (where Chainlit runs) to a port on your host machine (e.g., 7860).
     ```bash
     docker run -p 7860:7860 -p 8082:8082 axiom-2.0
     ```

chainlit.md

@@ -3,18 +3,17 @@
 Axiom 2.0 is an **advanced AI Agent** built to assist developers in navigating the complex landscape of modern AI and software development. Leveraging the **OpenAI Agents SDK**, **Model Context Protocol (MCP)**, Chainlit, and Gemini models, Axiom 2.0 specializes in generating accurate, production-ready code and providing expert guidance across areas like agent architectures, RAG systems, authentication, vector databases, and full-stack development.
 
 Axiom 2.0 is an upgrade of Axiom 1.0, it has the following more features:
-1. **Mandatory Planning & Documentation:** Axiom 2.0 uses a structured planning phase before generating code and projects. This ensures higher accuracy and adherence to best practices.
-2. **Production-Ready Code Focus:** Axiom 2.0's primary goal is to generate production-quality, modular, efficient, scalable, and readable code, including end-to-end projects and full-stack applications.
-3. **Vast Library Database:** It has access to a vast library database (**2000+** libraries, frameworks, and tools) unlike it's predecessor, Axiom 1.0 which had only 20 libraries documentation.
+1. **Production-Ready Code Focus:** Axiom 2.0's primary goal is to generate production-quality, modular, efficient, scalable, and readable code, including end-to-end projects and full-stack applications.
+2. **Vast Library Database:** It has access to a vast library database (**2000+** libraries, frameworks, and tools) unlike it's predecessor, Axiom 1.0 which had only 20 libraries documentation.
 
 ## Features
 
 *   🤖 **Interactive Chat Interface:** Built with Chainlit for a smooth conversational experience.
 *   📚 **Advanced Documentation Access:** Utilizes a dedicated MCP server to retrieve and apply technical information from various sources, ensuring code and explanations are accurate and up-to-date.
-*   🧠 **Strategic Planning:** Employs a **Sequential Thinking MCP** tool to break down complex requests, plan code architecture, and structure its responses effectively.
 *   🧪 **Two Modes:** 
     1. **Agent Mode:** Ideal for complex tasks like end-to-end projects and full-stack development.
     2. **Assistant Mode:** Suited for quick information retrieval and answering questions.
+*   🧠 **Web Search:** It uses Tavily MCP server to search the web and extract content from URLs.
 *   📦 **Containerized Deployment:** Includes a `Dockerfile` for easy deployment to platforms like Hugging Face Spaces or Docker environments.
 
 ## Project Code