Enhance README with detailed local setup instructions, clarify integration with MCP clients, and update code quality score description. Remove outdated sections and improve formatting for better readability.

README.md

@@ -16,48 +16,10 @@ short_description: Generate quality metrics and a detailed report for your code
 
 This project is a Gradio-based MCP server that provides two code analysis functionalities:
 
--  **Code Quality Score**: Provides an averaged score across vulnerability, style, and quality for the provided code using top three AI providers.
+-  **Code Quality Score**: Provides an averaged score across vulnerability, style, and quality for the provided code using top three AI providers (OpenAI, Anthropic, Mistral).
 -  **Code Analysis Report**: Generates a detailed report about the provided code, including basic information and suggesting 5-10 potential fixes to improve the code.
 
-## Setup and Running
-
-1.  Clone the repository.
-2.  Navigate to the project directory.
-3.  Install the required dependencies:
-
-    ```bash
-    pip install -r requirements.txt
-    ```
-
-4.  Run the application:
-
-    ```bash
-    python src/app.py
-    ```
-
-5.  The Gradio interface will be available at `http://127.0.0.1:7860/` and MCP server will be avaible at `http://127.0.0.1:7860/gradio_api/mcp/sse`.
-
-## Connecting to Cursor AI
-
-7. To test the MCP server with Cursor AI, open Cursor Settings, navigate to the "MCP" tab, and click the "+ Add new global MCP server" button.
-
-8. Add the following JSON configuration to the MCP settings file:
-```json
-{
-  "mcpServers": {
-    "gradio": {
-      "url": "http://127.0.0.1:7860/gradio_api/mcp/sse"
-    }
-  }
-}
-```
-
-9. Save the file. You will now see an active MCP server named `gradio` with the tools `code_analysis_report` and `code_analysis_score`. 
-
-
-To test this MCP server, you can create a new chat in agent mode of the Cursor using (CTRL +T) and ask for a code analysis report (e.g., "analyze this Python code: print('hello')"). Cursor will ask for permission to run the MCP tool. Approve it.
-
-### Integration with other clients
+## Integration with MCP clients
 
 For clients that support SSE (e.g. Cursor, Windsurf, Cline), simply add the following configuration to your MCP config:
 
@@ -71,7 +33,7 @@ For clients that support SSE (e.g. Cursor, Windsurf, Cline), simply add the foll
 }
 ```
 
-For clients that only support stdio, first install Node.js. Then, you can use the following command:
+For clients that dose not support SSE, first install Node.js. Then, you can use the following command:
 
 ```json
 {
@@ -89,7 +51,7 @@ For clients that only support stdio, first install Node.js. Then, you can use th
 }
 ```
 
-### Sample Prompts
+## Sample Prompts
 
 Here are a few ways you can ask Cursor AI to use these tools:
 
@@ -98,4 +60,53 @@ Here are a few ways you can ask Cursor AI to use these tools:
 *   "Analyze this code and tell me how to fix the top issues."
 *   "What is the quality score of this code?"
 
+## Local Setup and Running
+
+1.  Clone the repository.
+2.  Navigate to the project directory.
+3.  Install the required dependencies:
+
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+4. Set up the required environment variables for the API keys:
+
+    ```bash
+    export OPENAI_API_KEY=your_openai_api_key
+    export ANTHROPIC_API_KEY=your_anthropic_api_key
+    export MISTRAL_API_KEY=your_mistral_api_key
+    ```
+
+   Replace `your_openai_api_key`, `your_anthropic_api_key`, and `your_mistral_api_key` with your actual API keys.
+
+5. Run the application:
+
+    ```bash
+    python src/app.py
+    ```
+
+6.  The Gradio interface will be available at `http://127.0.0.1:7860/` and MCP server will be avaible at `http://127.0.0.1:7860/gradio_api/mcp/sse`.
+
+## Connecting to Cursor AI
+
+7. To test the MCP server with Cursor AI, open Cursor Settings, navigate to the "MCP" tab, and click the "+ Add new global MCP server" button.
+
+8. Add the following JSON configuration to the MCP settings file:
+```json
+{
+  "mcpServers": {
+    "gradio": {
+      "url": "http://127.0.0.1:7860/gradio_api/mcp/sse"
+    }
+  }
+}
+```
+
+9. Save the file. You will now see an active MCP server named `gradio` with the tools `code_analysis_report` and `code_analysis_score`. 
+
+
+To test this MCP server, you can create a new chat in agent mode of the Cursor using (CTRL +T) and ask for a code analysis report (e.g., "analyze this Python code: print('hello')"). Cursor will ask for permission to run the MCP tool. Approve it.
+
+
 

requirements.txt

@@ -1,4 +1,3 @@
-python-dotenv>=1.1.0
 mistralai==1.8.1
 openai==1.84.0
 anthropic==0.52.2

src/app.py

@@ -1,11 +1,8 @@
 import gradio as gr
-from dotenv import load_dotenv
 
 from code_analyzer.analysis import code_analysis_report
 from code_analyzer.scoring import code_analysis_score
 
-load_dotenv()
-
 
 # Create Gradio interfaces for code analysis
 analysis_report_demo = gr.Interface(
@@ -25,8 +22,8 @@ code_score_demo = gr.Interface(
 # Create tabbed interface
 demo = gr.TabbedInterface(
     [analysis_report_demo, code_score_demo],
-    ["🧐Code Analysis", "💯Code Score"],
-    title="🧑‍💻Code Scoring & Analysis MCP Server",
+    ["🧐Code Analysis", "🥇Code Score"],
+    title="Code Scoring & Analysis MCP Server",
     theme=gr.themes.Soft(),
 )