Update README.md to enhance project description, setup instructions, and connection details for the Code Analysis MCP server. Adjusted title, emoji, and SDK version, and clarified usage with Cursor AI.

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# UV
+#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#uv.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+# Abstra
+# Abstra is an AI-powered process automation framework.
+# Ignore directories containing user credentials, local state, and settings.
+# Learn more at https://abstra.io/docs
+.abstra/
+
+# Visual Studio Code
+#  Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore 
+#  that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore
+#  and can be added to the global gitignore or merged into this file. However, if you prefer, 
+#  you could uncomment the following to ignore the enitre vscode folder
+# .vscode/
+
+# Ruff stuff:
+.ruff_cache/
+
+# PyPI configuration file
+.pypirc
+
+# Cursor
+#  Cursor is an AI-powered code editor. `.cursorignore` specifies files/directories to
+#  exclude from AI features like autocomplete and code analysis. Recommended for sensitive data
+#  refer to https://docs.cursor.com/context/ignore-files
+.cursorignore
+.cursorindexingignore

LICENSE

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README.md

@@ -1,14 +1,69 @@
 ---
-title: Code Analysis Mcp
-emoji: 🐠
-colorFrom: gray
-colorTo: pink
+title: Code Analysis MCP
+emoji: 🧑‍💻
+colorFrom: black
+colorTo: yellow
 sdk: gradio
-sdk_version: 5.33.0
-app_file: app.py
+sdk_version: 5.32.1
+app_file: src/app.py
 pinned: false
 license: apache-2.0
 short_description: Generate quality metrics and a detailed report for your code
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+# Code Analysis MCP Server
+
+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 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.
+
+### Sample Prompts
+
+Here are a few ways you can ask Cursor AI to use these tools:
+
+*   "Can you give me a code quality score for this Python snippet?"
+*   "Generate a code analysis report for the following JavaScript code."
+*   "Analyze this code and tell me how to fix the top issues."
+*   "What is the quality score of this code?"
+
+

requirements.txt

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+python-dotenv>=1.1.0
+gradio==5.32.1
+gradio[mcp]==5.32.1
+mistralai==1.8.1
+openai==1.84.0
+anthropic==0.52.2

src/app.py

@@ -0,0 +1,34 @@
+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(
+    fn=code_analysis_report,
+    inputs=gr.Textbox(label="Enter Code Here", lines=20),
+    outputs=gr.Textbox(label="Analysis Report", lines=20),
+    description="Generate a basic code analysis report.",
+)
+
+code_score_demo = gr.Interface(
+    fn=code_analysis_score,
+    inputs=gr.Textbox(label="Enter Code Here", lines=20),
+    outputs=gr.JSON(label="Code Score"),
+    description="Generate a basic code score.",
+)
+
+# Create tabbed interface
+demo = gr.TabbedInterface(
+    [analysis_report_demo, code_score_demo],
+    ["Code Analysis Report", "Code Score"],
+    title="Code Analysis Server",
+)
+
+if __name__ == "__main__":
+    # Launch the Gradio interface
+    demo.launch(share=False, mcp_server=True, debug=True)

src/code_analyzer/__init__.py

@@ -0,0 +1 @@
+ 

src/code_analyzer/analysis.py

@@ -0,0 +1,123 @@
+import os
+
+from anthropic import Anthropic
+
+
+def code_analysis_report(code: str) -> str:
+    """
+    Generate a code analysis report using the Anthropic API.
+
+    Args:
+        code (str): The code string to analyze.
+
+    Returns:
+        str: A detailed analysis report including top fixes.
+    """
+    if not code:
+        return "Please provide code to analyze."
+
+    try:
+        client = Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])
+
+        analysis_prompt = (
+            """Analyze the following code for potential issues, including vulnerabilities, style problems, and quality concerns. """.strip()
+            + f""" Provide a detailed analysis and list the top 5-10 most critical fixes needed, explaining each one.
+```python
+{code}
+```""".strip()
+        )
+
+        # Define a tool to structure the output
+        tools = [
+            {
+                "name": "analysis_report",
+                "description": "Return the detailed analysis and a list of top fixes.",
+                "input_schema": {
+                    "type": "object",
+                    "properties": {
+                        "detailed_analysis": {
+                            "type": "string",
+                            "description": "Detailed analysis of the code.",
+                        },
+                        "top_fixes": {
+                            "type": "array",
+                            "description": "List of top fixes needed.",
+                            "items": {
+                                "type": "object",
+                                "properties": {
+                                    "fix_description": {
+                                        "type": "string",
+                                        "description": "Description of the fix.",
+                                    },
+                                    "severity": {
+                                        "type": "string",
+                                        "description": "Severity of the issue (e.g., Critical, High, Medium, Low).",
+                                    },
+                                },
+                                "required": ["fix_description", "severity"],
+                            },
+                        },
+                    },
+                    "required": ["detailed_analysis", "top_fixes"],
+                    "additionalProperties": False,
+                },
+            }
+        ]
+
+        resp = client.messages.create(
+            model="claude-sonnet-4-20250514",  # Using the model from scoring.py
+            messages=[{"role": "user", "content": analysis_prompt.format(code=code)}],
+            system="You are a secure-coding assistant that provides detailed analysis and actionable fixes.",
+            tools=tools,
+            tool_choice={"type": "tool", "name": "analysis_report"},
+            max_tokens=1500,  # Increased tokens for detailed output
+            temperature=0,
+        )
+
+        # Check if the response contains a tool_use block
+        tool_call = None
+        for content_block in resp.content:
+            if content_block.type == "tool_use":
+                tool_call = content_block
+                break
+
+        if tool_call is None:
+            # If no tool_use block is found, return an error with the raw response content
+            return f"Error during code analysis: Expected tool_use response, but received: {resp.content}"
+
+        analysis_result = tool_call.input
+
+        report = "Code Analysis Report:\n\n"
+        report += "Detailed Analysis:\n"
+        report += analysis_result.get("detailed_analysis", "N/A") + "\n\n"
+
+        report += "Top Fixes:\n"
+        fixes = analysis_result.get("top_fixes", [])
+        if fixes:
+            for i, fix in enumerate(fixes):
+                report += f"{i + 1}. [Severity: {fix.get('severity', 'N/A')}] {fix.get('fix_description', 'N/A')}\n"
+        else:
+            report += "No specific fixes identified or issues found."
+
+        return report
+
+    except Exception as exc:
+        return f"Error during code analysis: {exc}"
+
+
+# ------------------------------------------------------------------ #
+# Demo / quick test
+# ------------------------------------------------------------------ #
+
+if __name__ == "__main__":
+    sample_code = """
+def calculate_discount(price, discount_rate):
+    # Applies a discount to a price
+    if discount_rate > 1:
+        return price # No discount if rate is invalid
+    return price * (1 - discount_rate)
+"""
+
+    print("Analyzing sample code...")
+    analysis_report = code_analysis_report(sample_code)
+    print("\n" + analysis_report)

src/code_analyzer/scoring.py

@@ -0,0 +1,246 @@
+import json
+import os
+from typing import Any, Dict
+
+from anthropic import Anthropic
+from mistralai import Mistral
+from openai import OpenAI
+from pydantic import BaseModel, ValidationError
+
+# ------------------------------------------------------------------ #
+# Configuration
+# ------------------------------------------------------------------ #
+
+DEFAULT_SCORES: Dict[str, Any] = {
+    "vulnerability_score": 0,
+    "style_score": 0,
+    "quality_score": 0,
+}
+
+ANALYSIS_PROMPT_TEMPLATE = (
+    "Analyze the following code for vulnerabilities, style, and quality "
+    "and return **only** a JSON object with keys "
+    "'vulnerability_score', 'style_score', and 'quality_score' "
+    "(each 0–100):\n```python\n{code}\n```"
+)
+
+SYSTEM_MESSAGES = {
+    "anthropic": "You are a secure-coding assistant. Assess code quality, style and vulnerabilities.",
+    "mistral": "You are a secure-coding assistant. Assess code quality, style and vulnerabilities.",
+    "openai": "You are a secure-coding assistant. Assess code quality, style and vulnerabilities.",
+}
+
+MODELS = {
+    "anthropic": "claude-sonnet-4-20250514",
+    "mistral": "mistral-medium-2505",
+    "openai": "gpt-4.1-2025-04-14",
+}
+
+REQUIRED_KEYS = ("vulnerability_score", "style_score", "quality_score")
+
+# ------------------------------------------------------------------ #
+# Helpers
+# ------------------------------------------------------------------ #
+
+
+class CodeAnalysisResult(BaseModel):
+    vulnerability_score: int
+    style_score: int
+    quality_score: int
+
+
+def _safe_json_loads(raw: str) -> Dict[str, Any]:
+    """
+    Best-effort JSON parsing – fall back to DEFAULT_SCORES on failure.
+    """
+    try:
+        return json.loads(raw)
+    except json.JSONDecodeError:
+        return DEFAULT_SCORES.copy()
+
+
+def _ensure_all_keys(d: dict, default: int = 0) -> dict:
+    """
+    Return a dict that has every REQUIRED_KEYS entry.
+    Missing keys are added with `default`.
+    Non-required keys are discarded.
+    """
+    return {key: int(d.get(key, default)) for key in REQUIRED_KEYS}
+
+
+# ------------------------------------------------------------------ #
+# Provider wrappers
+# ------------------------------------------------------------------ #
+
+
+def analyze_code_anthropic(code: str) -> dict:
+    if not code:
+        return _ensure_all_keys({})
+
+    try:
+        client = Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])
+        prompt = ANALYSIS_PROMPT_TEMPLATE.format(code=code)
+
+        tools = [
+            {
+                "name": "code_scores",
+                "description": "Return ONLY the three integer scores (0-100).",
+                "input_schema": {
+                    "type": "object",
+                    "properties": {
+                        "vulnerability_score": {
+                            "type": "integer",
+                            "minimum": 0,
+                            "maximum": 100,
+                        },
+                        "style_score": {
+                            "type": "integer",
+                            "minimum": 0,
+                            "maximum": 100,
+                        },
+                        "quality_score": {
+                            "type": "integer",
+                            "minimum": 0,
+                            "maximum": 100,
+                        },
+                    },
+                    "required": list(REQUIRED_KEYS),
+                    "additionalProperties": False,
+                },
+            }
+        ]
+
+        resp = client.messages.create(
+            model=MODELS["anthropic"],
+            messages=[{"role": "user", "content": prompt}],
+            system=SYSTEM_MESSAGES["anthropic"],
+            tools=tools,
+            tool_choice={"type": "tool", "name": "code_scores"},
+            max_tokens=130,
+            temperature=0,
+        )
+
+        tool_call = next(c for c in resp.content if c.type == "tool_use")
+        return _ensure_all_keys(tool_call.input)
+
+    except Exception as exc:
+        out = _ensure_all_keys({})
+        out["error"] = f"Anthropic API error: {exc}"
+        return out
+
+
+def analyze_code_mistral(code: str) -> Dict[str, Any]:
+    if not code:
+        return DEFAULT_SCORES.copy()
+
+    try:
+        client = Mistral(api_key=os.environ["MISTRAL_API_KEY"])
+        prompt = ANALYSIS_PROMPT_TEMPLATE.format(code=code)
+
+        resp = client.chat.complete(
+            model=MODELS["mistral"],
+            messages=[
+                {"role": "system", "content": SYSTEM_MESSAGES["mistral"]},
+                {"role": "user", "content": prompt},
+            ],
+            response_format={"type": "json_object"},
+        )
+
+        return _safe_json_loads(resp.choices[0].message.content)
+
+    except Exception as exc:
+        result = DEFAULT_SCORES.copy()
+        result["error"] = f"Mistral API error: {exc}"
+        return result
+
+
+def analyze_code_openai(code: str) -> Dict[str, Any]:
+    if not code:
+        return DEFAULT_SCORES.copy()
+
+    try:
+        client = OpenAI()  # uses OPENAI_API_KEY from env
+        prompt = ANALYSIS_PROMPT_TEMPLATE.format(code=code)
+
+        resp = client.chat.completions.create(
+            model=MODELS["openai"],
+            messages=[
+                {"role": "system", "content": SYSTEM_MESSAGES["openai"]},
+                {"role": "user", "content": prompt},
+            ],
+            response_format={"type": "json_object"},
+        )
+
+        # Validate via Pydantic (optional but nice)
+        parsed = _safe_json_loads(resp.choices[0].message.content)
+        try:
+            validated = CodeAnalysisResult(**parsed)
+            return validated.model_dump()
+        except ValidationError:
+            # If model returns extra fields or wrong types, fall back to raw
+            return parsed
+
+    except Exception as exc:
+        result = DEFAULT_SCORES.copy()
+        result["error"] = f"OpenAI API error: {exc}"
+        return result
+
+
+# ------------------------------------------------------------------ #
+# Aggregator
+# ------------------------------------------------------------------ #
+
+
+def code_analysis_score(code: str) -> Dict[str, Any]:
+    """
+    Analyzes the provided code string using multiple AI providers and returns an
+    averaged score across vulnerability, style, and quality.
+
+    Args:
+        code: The code string to analyze.
+
+    Returns:
+        A dictionary containing the averaged vulnerability, style, and quality scores,
+        or an error message if all providers fail.
+    """
+    if not code:
+        return DEFAULT_SCORES.copy()
+
+    scores_list = [
+        analyze_code_anthropic(code),
+        analyze_code_mistral(code),
+        analyze_code_openai(code),
+    ]
+    valid = [s for s in scores_list if "error" not in s]
+
+    if not valid:
+        result = DEFAULT_SCORES.copy()
+        result["error"] = "All API providers failed"
+        return result
+
+    # Average
+    averaged = {
+        "vulnerability_score": sum(s["vulnerability_score"] for s in valid)
+        // len(valid),
+        "style_score": sum(s["style_score"] for s in valid) // len(valid),
+        "quality_score": sum(s["quality_score"] for s in valid) // len(valid),
+    }
+    return averaged
+
+
+# ------------------------------------------------------------------ #
+# Demo / quick test
+# ------------------------------------------------------------------ #
+
+if __name__ == "__main__":
+    sample = """
+def example_function(x):
+    if x is None:
+        return "Error"
+    return x * 2
+"""
+
+    print("Anthropic →", analyze_code_anthropic(sample))
+    print("Mistral   →", analyze_code_mistral(sample))
+    print("OpenAI    →", analyze_code_openai(sample))
+    print("AVERAGED  →", code_analysis_score(sample))

tests/test_app.py

@@ -0,0 +1,18 @@
+from gradio_client import Client
+
+client = Client("http://127.0.0.1:7860/")
+
+# Test case for Code Analysis Report
+code_for_analysis = "print('Hello, world!')"
+result_report = client.predict(code=code_for_analysis, api_name="/predict")
+print("Code Analysis Report result:", result_report)
+assert (
+    isinstance(result_report, str)
+    and "Analysis Report for provided code:" in result_report
+)
+
+# Test case for Code Score
+code_for_score = "def my_function():\n  pass"
+result_score = client.predict(code=code_for_score, api_name="/predict_1")
+print("Code Score result:", result_score)
+assert isinstance(result_score, str) and "Code Score for provided code:" in result_score