From c88cec2681cd0be8554434800d654e74eb802943 Mon Sep 17 00:00:00 2001
From: Svetlana Perekrestova <svetlana.perekrestova2@gmail.com>
Date: Fri, 16 Jan 2026 21:22:56 +0100
Subject: [PATCH] Fixing communication issue

---
 backend/{ => .claude}/CLAUDE.md | 159 ++++++-
 backend/.env.example            |  10 +-
 backend/.gitignore              |   3 +
 backend/app/config.py           |   5 +-
 backend/app/llm.py              |  67 ++-
 backend/app/mcp.py              | 142 ++++--
 backend/pyproject.toml          |   1 +
 backend/uv.lock                 | 782 +++++++++++++++++++++++++++++++-
 8 files changed, 1077 insertions(+), 92 deletions(-)
 rename backend/{ => .claude}/CLAUDE.md (56%)

diff --git a/backend/CLAUDE.md b/backend/.claude/CLAUDE.md
similarity index 56%
rename from backend/CLAUDE.md
rename to backend/.claude/CLAUDE.md
index 8d6a78d..7d21cdb 100644
--- a/backend/CLAUDE.md
+++ b/backend/.claude/CLAUDE.md
@@ -6,13 +6,14 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
 
 This is the FastAPI backend for the GitHub Knowledge Vault project. It provides:
 - **REST API** for repository browsing and documentation retrieval
-- **WebSocket Chat** for real-time AI conversations with Claude
+- **WebSocket Chat** for real-time AI conversations with LLM
 - **MCP Integration** for external documentation server communication
-- **Streaming LLM** responses with automatic tool execution
+- **Streaming LLM** responses with automatic tool execution via litellm
+- **Multi-provider support** (OpenRouter, Anthropic, OpenAI, 100+ models)
 
 The backend uses a 3-layer architecture:
 1. **FastAPI Layer** (`app/main.py`) - HTTP/WebSocket endpoints
-2. **LLM Layer** (`app/llm.py`) - Claude streaming + tool orchestration
+2. **LLM Layer** (`app/llm.py`) - litellm streaming + tool orchestration
 3. **MCP Layer** (`app/mcp.py`) - External MCP server communication
 
 ## Development Setup
@@ -42,8 +43,13 @@ uv add --dev package-name
 
 ### Environment Configuration
 1. Copy `.env.example` to `.env`
-2. Add your `ANTHROPIC_API_KEY` (required)
-3. Configure optional settings (Claude model, MCP server URL, CORS origins)
+2. Add your `OPENROUTER_API_KEY` (required - get at https://openrouter.ai/keys)
+3. Configure optional settings (model name, MCP server URL, CORS origins)
+
+**Free Models Available:**
+- `openrouter/meta-llama/llama-3.3-70b-instruct` - Best overall (recommended)
+- `openrouter/qwen/qwen-coder-turbo` - Best for code
+- `openrouter/deepseek/deepseek-r1` - Best for reasoning
 
 ### Running the Application
 
@@ -65,11 +71,26 @@ Run tests:
 uv run pytest
 ```
 
+Run tests with verbose output:
+```bash
+uv run pytest -v
+```
+
 Run tests with coverage:
 ```bash
 uv run pytest --cov=app
 ```
 
+Run specific test file:
+```bash
+uv run pytest tests/test_rest_api.py -v
+```
+
+**Test Configuration:**
+- Tests use a mock MCP server on port 3002
+- pytest.ini is configured for asyncio auto mode
+- All 24 tests must pass before committing
+
 ### Docker
 
 Build the image:
@@ -92,11 +113,14 @@ docker run -p 3001:3001 --env-file .env github-knowledge-vault-backend
 - Conversation management (in-memory storage)
 - Application lifespan management (startup/shutdown)
 - CORS middleware configuration
+- Health check with service status
 
 **2. LLM Layer** (`app/llm.py`)
-- Claude API integration with streaming support
-- Tool definitions for documentation operations
+- **litellm** integration for multi-provider support
+- Streaming chat with OpenAI-compatible API
+- Tool definitions in OpenAI format (required by litellm)
 - Automatic tool execution loop
+- Event translation layer (maintains WebSocket compatibility)
 - System prompt builder with repository context
 - Singleton `llm_client` instance
 
@@ -110,7 +134,7 @@ docker run -p 3001:3001 --env-file .env github-knowledge-vault-backend
 
 **REST Endpoints**
 - `GET /` - Root endpoint
-- `GET /health` - Health check with service status
+- `GET /health` - Health check with service status (provider, model, MCP)
 - `GET /config` - Current configuration (excluding secrets)
 - `GET /api/repos` - List all repositories
 - `GET /api/repos/{name}/tree` - Get repository file tree
@@ -119,7 +143,7 @@ docker run -p 3001:3001 --env-file .env github-knowledge-vault-backend
 - `GET /api/conversations/{id}/messages` - Get conversation history
 
 **WebSocket Endpoint**
-- `WS /ws/chat/{conversation_id}` - Real-time chat with Claude
+- `WS /ws/chat/{conversation_id}` - Real-time chat with streaming LLM
 
 ### Configuration Pattern
 The application uses Pydantic Settings (`app/config.py`) for environment-based configuration:
@@ -131,10 +155,11 @@ The application uses Pydantic Settings (`app/config.py`) for environment-based c
 ### Key Dependencies
 - **FastAPI**: Web framework
 - **Uvicorn**: ASGI server
-- **Anthropic SDK**: Claude API integration with streaming
+- **litellm**: Multi-provider LLM library (OpenRouter, Anthropic, OpenAI, 100+ models)
 - **httpx**: Async HTTP client for MCP server communication
 - **Pydantic Settings**: Configuration management
 - **WebSockets**: Real-time communication support
+- **pytest**: Testing framework with asyncio support
 
 ## Common Patterns
 
@@ -143,18 +168,21 @@ The application uses Pydantic Settings (`app/config.py`) for environment-based c
 from app.config import settings
 
 # Access configuration
-api_key = settings.ANTHROPIC_API_KEY
-model = settings.CLAUDE_MODEL
+api_key = settings.OPENROUTER_API_KEY
+model = settings.MODEL_NAME
+api_base = settings.API_BASE
+max_tokens = settings.MAX_TOKENS
 ```
 
 ### Environment Variables
 All configuration is environment-driven. See `.env.example` for available options:
-- `ANTHROPIC_API_KEY` (required)
-- `CLAUDE_MODEL` (optional, defaults to claude-sonnet-4-20250514)
-- `CLAUDE_MAX_TOKENS` (optional, defaults to 4096)
-- `MCP_SERVER_URL` (optional, defaults to http://mcp-server:3000)
-- `MCP_TIMEOUT` (optional, defaults to 30 seconds)
-- `CORS_ORIGINS` (optional, defaults to localhost development ports)
+- `OPENROUTER_API_KEY` (required) - Get at https://openrouter.ai/keys
+- `MODEL_NAME` (optional) - defaults to `openrouter/meta-llama/llama-3.3-70b-instruct`
+- `API_BASE` (optional) - defaults to `https://openrouter.ai/api/v1`
+- `MAX_TOKENS` (optional) - defaults to 4096
+- `MCP_SERVER_URL` (optional) - defaults to `http://mcp-server:3000`
+- `MCP_TIMEOUT` (optional) - defaults to 30 seconds
+- `CORS_ORIGINS` (optional) - defaults to localhost development ports
 
 ## Module Documentation
 
@@ -164,11 +192,21 @@ Pydantic Settings for environment-based configuration:
 from app.config import settings
 
 # Access any setting
-api_key = settings.ANTHROPIC_API_KEY
-model = settings.CLAUDE_MODEL
+api_key = settings.OPENROUTER_API_KEY
+model = settings.MODEL_NAME
+api_base = settings.API_BASE
 cors = settings.CORS_ORIGINS
 ```
 
+**Settings:**
+- `OPENROUTER_API_KEY`: API key for OpenRouter
+- `MODEL_NAME`: Model identifier (e.g., `openrouter/meta-llama/llama-3.3-70b-instruct`)
+- `API_BASE`: API base URL for litellm
+- `MAX_TOKENS`: Maximum tokens for LLM responses
+- `MCP_SERVER_URL`: URL for MCP server
+- `MCP_TIMEOUT`: Timeout for MCP requests in seconds
+- `CORS_ORIGINS`: List of allowed CORS origins
+
 ### `app/mcp.py` - MCP Client
 HTTP client for MCP server communication:
 ```python
@@ -195,7 +233,7 @@ await mcp_client.disconnect()
 - `list_repo_docs` - List all docs in a repository (requires: `repo`)
 
 ### `app/llm.py` - LLM Client
-Claude API client with streaming and tool execution:
+Multi-provider LLM client using litellm with streaming and tool execution:
 ```python
 from app.llm import llm_client
 
@@ -213,7 +251,7 @@ async for event in llm_client.chat_stream(messages, context):
 ```
 
 **Event Types:**
-- `{"type": "text", "content": "..."}` - Text chunk from Claude
+- `{"type": "text", "content": "..."}` - Text chunk from LLM
 - `{"type": "tool_use_start", "toolId": "...", "name": "...", "input": {...}}` - Tool execution begins
 - `{"type": "tool_result", "toolId": "...", "name": "...", "result": {...}, "duration": 123}` - Tool execution completes
 
@@ -222,12 +260,39 @@ async for event in llm_client.chat_stream(messages, context):
 - When `context.scope == "repo"`, adds repository-specific instructions
 - Automatically built with `build_system_prompt(context)`
 
+**Tool Format:**
+- Tools are defined in OpenAI format (required by litellm)
+- litellm handles conversion between provider formats automatically
+- Tools are executed via MCP client
+
+**Streaming:**
+- Uses litellm's `acompletion` with `stream=True`
+- Handles tool execution loop with continuation
+- Maintains backward-compatible event format for WebSocket clients
+
 ### `app/main.py` - FastAPI Application
 Main application with REST and WebSocket endpoints:
 ```python
 # See API Endpoints section above for available routes
 ```
 
+**Health Check:**
+Returns provider, model, and service status:
+```json
+{
+  "status": "healthy",
+  "version": "1.0.0",
+  "services": {
+    "mcp_server": {"status": "connected"},
+    "llm_api": {
+      "status": "available",
+      "provider": "openrouter",
+      "model": "openrouter/meta-llama/llama-3.3-70b-instruct"
+    }
+  }
+}
+```
+
 ## WebSocket Protocol
 
 ### Connection
@@ -265,7 +330,7 @@ const ws = new WebSocket(`ws://localhost:3001/ws/chat/${conversationId}`);
 ```json
 {
   "type": "tool_use_start",
-  "toolId": "toolu_123",
+  "toolId": "tool_123",
   "name": "list_repositories",
   "input": {}
 }
@@ -275,7 +340,7 @@ const ws = new WebSocket(`ws://localhost:3001/ws/chat/${conversationId}`);
 ```json
 {
   "type": "tool_result",
-  "toolId": "toolu_123",
+  "toolId": "tool_123",
   "name": "list_repositories",
   "result": [...],
   "duration": 234
@@ -296,3 +361,49 @@ const ws = new WebSocket(`ws://localhost:3001/ws/chat/${conversationId}`);
 ```json
 {"type": "pong"}
 ```
+
+## Testing
+
+### Test Structure
+- `tests/test_rest_api.py` - REST endpoint tests (health, repos, conversations)
+- `tests/test_websocket.py` - WebSocket chat tests with mocked LLM
+- `tests/test_integration.py` - End-to-end workflow tests
+- `tests/test_error_scenarios.py` - Error handling and edge cases
+- `tests/conftest.py` - Pytest fixtures and configuration
+- `tests/mock_mcp_server.py` - Mock MCP server for testing
+
+### Running Tests in IDE
+1. Configure pytest as test runner in IDE settings
+2. Mark `tests` directory as Test Sources Root
+3. Set environment variables in run configuration:
+   - `MCP_SERVER_URL=http://localhost:3002`
+   - `OPENROUTER_API_KEY=test-key-12345`
+4. Run/debug individual tests by clicking play button in gutter
+
+### Test Coverage
+All 24 tests must pass:
+- ✅ 4 error scenario tests
+- ✅ 8 integration workflow tests
+- ✅ 10 REST API tests
+- ✅ 6 WebSocket tests
+
+## Migration Notes
+
+### From Anthropic SDK to litellm
+The backend was migrated from direct Anthropic SDK to litellm for:
+- **Multi-provider support**: OpenRouter, Anthropic, OpenAI, 100+ models
+- **Cost reduction**: Access to free models via OpenRouter
+- **Flexibility**: Easy model switching via environment variables
+
+**Key Changes:**
+- `ANTHROPIC_API_KEY` → `OPENROUTER_API_KEY`
+- `CLAUDE_MODEL` → `MODEL_NAME`
+- Tool format: Anthropic format → OpenAI format (required by litellm)
+- Streaming: `anthropic.messages.stream()` → `litellm.acompletion(stream=True)`
+- Event format: Maintained backward compatibility via translation layer
+
+**Benefits:**
+- Free tier models available (Llama 3.3 70B, Qwen Coder, DeepSeek)
+- Can switch between providers without code changes
+- Same WebSocket protocol for frontend compatibility
+- All tests continue to pass
diff --git a/backend/.env.example b/backend/.env.example
index 616e33b..bfb3d9d 100644
--- a/backend/.env.example
+++ b/backend/.env.example
@@ -8,5 +8,13 @@ OPENROUTER_API_KEY=sk-or-v1-your_key_here
 MODEL_NAME=openrouter/meta-llama/llama-3.3-70b-instruct
 
 MAX_TOKENS=4096
-MCP_SERVER_URL=http://mcp-server:3000
+
+# MCP Server Configuration
+# For local development: http://localhost:8003/mcp
+# For Docker: http://mcp-server:3000/mcp
+# For Hugging Face: https://your-space.hf.space/mcp
+MCP_SERVER_URL=http://localhost:8003/mcp
 MCP_TIMEOUT=30
+
+# GitHub organization to query via MCP server
+GITHUB_ORG=your-org-name
diff --git a/backend/.gitignore b/backend/.gitignore
index 11c48f6..b04cff5 100644
--- a/backend/.gitignore
+++ b/backend/.gitignore
@@ -42,3 +42,6 @@ Thumbs.db
 
 # Logs
 *.log
+
+# Claude Code local settings
+.claude/settings.local.json
diff --git a/backend/app/config.py b/backend/app/config.py
index 8574529..042577b 100644
--- a/backend/app/config.py
+++ b/backend/app/config.py
@@ -15,9 +15,12 @@ class Settings(BaseSettings):
     API_BASE: str = "https://openrouter.ai/api/v1"
     MAX_TOKENS: int = 4096
 
-    MCP_SERVER_URL: str = "http://mcp-server:3000"
+    MCP_SERVER_URL: str = "https://sperva-github-mcp-server.hf.space"
     MCP_TIMEOUT: int = 30
 
+    # GitHub organization for MCP server
+    GITHUB_ORG: str = "lifter-ai"
+
     CORS_ORIGINS: List[str] = ["http://localhost:5173", "http://localhost:3000"]
 
 
diff --git a/backend/app/llm.py b/backend/app/llm.py
index 684e8fc..6c1e5f3 100644
--- a/backend/app/llm.py
+++ b/backend/app/llm.py
@@ -12,13 +12,48 @@
 
 logger = logging.getLogger(__name__)
 
+
+def map_tool_call(tool_name: str, tool_input: dict) -> tuple[str, dict]:
+    """
+    Map LLM tool calls to MCP server tool names and parameters.
+
+    The LLM uses simplified tool names, but the MCP server expects
+    specific tool names with the 'org' parameter included.
+
+    Args:
+        tool_name: Tool name from LLM (e.g., 'list_repositories')
+        tool_input: Tool arguments from LLM
+
+    Returns:
+        Tuple of (mcp_tool_name, mcp_arguments)
+    """
+    org = settings.GITHUB_ORG
+
+    # Map LLM tool names to MCP tool names
+    tool_mapping = {
+        "list_repositories": "get_org_repos_tool",
+        "search_documentation": "search_documentation_tool",
+        "get_documentation": "get_file_content_tool",
+        "list_repo_docs": "get_repo_docs_tool"
+    }
+
+    mcp_tool_name = tool_mapping.get(tool_name, tool_name)
+
+    # Add org parameter to all tool calls
+    mcp_arguments = {"org": org, **tool_input}
+
+    logger.debug(f"Mapped {tool_name} -> {mcp_tool_name} with args: {mcp_arguments}")
+
+    return mcp_tool_name, mcp_arguments
+
 # Tool definitions in OpenAI format (required by litellm)
+# These tool names and parameters match the MCP server's actual tools
 TOOLS = [
     {
         "type": "function",
         "function": {
             "name": "list_repositories",
-            "description": "List all available repositories in the organization with their documentation counts.",
+            "description": "List all available repositories in the organization with their documentation counts. Searches for repositories that have a /doc folder.",
             "parameters": {
                 "type": "object",
                 "properties": {},
@@ -30,17 +65,13 @@
         "type": "function",
         "function": {
             "name": "search_documentation",
-            "description": "Search across all documentation. Returns matching documents with snippets.",
+            "description": "Search across documentation in all repositories using GitHub Code Search API. Returns matching files in /doc folders.",
             "parameters": {
                 "type": "object",
                 "properties": {
                     "query": {
                         "type": "string",
-                        "description": "Search query string"
-                    },
-                    "repo": {
-                        "type": "string",
-                        "description": "Optional: Limit search to specific repository"
+                        "description": "Search query string (e.g., 'authentication', 'API', 'streaming')"
                     }
                 },
                 "required": ["query"]
@@ -51,17 +82,17 @@
         "type": "function",
         "function": {
             "name": "get_documentation",
-            "description": "Retrieve the full content of a specific documentation file.",
+            "description": "Retrieve the full content of a specific documentation file from a repository.",
             "parameters": {
                 "type": "object",
                 "properties": {
                     "repo": {
                         "type": "string",
-                        "description": "Repository name"
+                        "description": "Repository name (not full path, just the repo name)"
                     },
                     "path": {
                         "type": "string",
-                        "description": "File path within the repository"
+                        "description": "File path within the repository (e.g., 'doc/README.md')"
                     }
                 },
                 "required": ["repo", "path"]
@@ -72,13 +103,13 @@
         "type": "function",
         "function": {
             "name": "list_repo_docs",
-            "description": "List all documentation files in a specific repository.",
+            "description": "List all documentation files in a specific repository's /doc folder. Filters for supported types: Markdown, Mermaid, SVG, OpenAPI, Postman.",
             "parameters": {
                 "type": "object",
                 "properties": {
                     "repo": {
                         "type": "string",
-                        "description": "Repository name"
+                        "description": "Repository name (not full path, just the repo name)"
                     }
                 },
                 "required": ["repo"]
@@ -240,12 +271,18 @@ async def chat_stream(
                             logger.error(f"Failed to parse tool arguments: {e}")
                             tool_input = {}
 
-                        # Execute tool
+                        # Map LLM tool call to MCP tool call
+                        mcp_tool_name, mcp_arguments = map_tool_call(
+                            tool_call['name'],
+                            tool_input
+                        )
+
+                        # Execute tool on MCP server
                         start_time = time.time()
                         try:
                             result = await mcp_client.call_tool(
-                                tool_call['name'],
-                                tool_input
+                                mcp_tool_name,
+                                mcp_arguments
                             )
                         except Exception as e:
                             logger.error(f"Tool execution error: {e}")
diff --git a/backend/app/mcp.py b/backend/app/mcp.py
index 908fb19..4ee3644 100644
--- a/backend/app/mcp.py
+++ b/backend/app/mcp.py
@@ -1,90 +1,136 @@
-import httpx
+"""
+MCP Client for communicating with FastMCP GitHub server.
+
+Uses the official FastMCP client library to communicate with the
+GitHub MCP Server via the Model Context Protocol over HTTP.
+"""
+
+import logging
 from typing import Any, Optional
+from contextlib import asynccontextmanager
+
+from fastmcp import Client
 from app.config import settings
 
+logger = logging.getLogger(__name__)
+
 
 class MCPClient:
-    """Minimal MCP client using HTTP."""
+    """MCP client using FastMCP's Client for standard MCP protocol communication."""
 
     def __init__(self):
-        self._client: Optional[httpx.AsyncClient] = None
+        self._client: Optional[Client] = None
         self._connected: bool = False
+        self._url: str = settings.MCP_SERVER_URL
 
     async def connect(self) -> None:
-        """Initialize HTTP client and test connection."""
-        self._client = httpx.AsyncClient(
-            base_url=settings.MCP_SERVER_URL,
-            timeout=settings.MCP_TIMEOUT
-        )
-
+        """Initialize MCP client and test connection."""
         try:
-            response = await self._client.get("/health")
-            self._connected = response.status_code == 200
-            print(f"MCP Server: {'Connected' if self._connected else 'Failed'}")
+            # Create FastMCP client with the MCP endpoint URL
+            self._client = Client(self._url)
+
+            # Test connection by listing available tools
+            async with self._client as client:
+                tools = await client.list_tools()
+                self._connected = True
+                logger.info(f"MCP Server: Connected ({len(tools)} tools available)")
+
+                # Log available tools for debugging
+                tool_names = [tool.name for tool in tools]
+                logger.debug(f"Available tools: {tool_names}")
+
         except Exception as e:
-            print(f"MCP Server connection failed: {e}")
+            logger.error(f"MCP Server connection failed: {e}")
             self._connected = False
 
     async def disconnect(self) -> None:
-        """Close HTTP client."""
-        if self._client:
-            try:
-                await self._client.aclose()
-            except (RuntimeError, Exception):
-                # Ignore errors during shutdown (e.g., event loop already closed)
-                pass
-            self._client = None
+        """Close MCP client connection."""
+        # FastMCP Client uses context manager, no explicit disconnect needed
+        self._client = None
         self._connected = False
+        logger.info("MCP client disconnected")
 
     @property
     def is_connected(self) -> bool:
-        return self._connected
+        return self._connected and self._client is not None
+
+    @asynccontextmanager
+    async def _get_client(self):
+        """Context manager to get an active client session."""
+        if not self._client:
+            raise RuntimeError("MCP client not initialized. Call connect() first.")
+
+        async with self._client as client:
+            yield client
 
     async def call_tool(self, name: str, arguments: dict) -> Any:
         """
-        Execute a tool on the MCP Server.
+        Execute a tool on the MCP Server using standard MCP protocol.
 
         Args:
-            name: Tool name (e.g., 'list_repositories')
+            name: Tool name (e.g., 'get_org_repos_tool', 'search_documentation_tool')
             arguments: Tool arguments dict
 
         Returns:
             Tool execution result
 
         Raises:
-            RuntimeError: If MCP call fails
+            RuntimeError: If MCP call fails or client not initialized
         """
-        if not self._client:
-            raise RuntimeError("MCP client not initialized")
-
         try:
-            response = await self._client.post(
-                "/tools/execute",
-                json={"name": name, "arguments": arguments}
-            )
-            response.raise_for_status()
-            data = response.json()
-            return data.get("result", data)
-
-        except httpx.TimeoutException:
-            raise RuntimeError(f"MCP tool '{name}' timed out")
-        except httpx.HTTPStatusError as e:
-            raise RuntimeError(f"MCP tool '{name}' failed: {e.response.text}")
+            async with self._get_client() as client:
+                logger.debug(f"Calling MCP tool: {name} with args: {arguments}")
+
+                # Call the tool using FastMCP client
+                result = await client.call_tool(name, arguments)
+
+                logger.debug(f"MCP tool '{name}' completed successfully")
+
+                # FastMCP returns a CallToolResult object with content
+                # Extract the actual data from the response
+                if hasattr(result, 'content') and result.content:
+                    # Get first content item (usually text)
+                    first_content = result.content[0]
+                    if hasattr(first_content, 'text'):
+                        # Try to parse as JSON if it's text
+                        import json
+                        try:
+                            return json.loads(first_content.text)
+                        except json.JSONDecodeError:
+                            return first_content.text
+                    return first_content
+
+                return result
+
+        except AttributeError as e:
+            raise RuntimeError(f"MCP client not initialized: {str(e)}")
         except Exception as e:
-            raise RuntimeError(f"MCP tool '{name}' error: {str(e)}")
+            logger.error(f"MCP tool '{name}' error: {str(e)}")
+            raise RuntimeError(f"MCP tool '{name}' failed: {str(e)}")
 
     async def health_check(self) -> dict:
-        """Check MCP Server health status."""
+        """
+        Check MCP Server health status.
+
+        Returns:
+            Status dictionary with connection state
+        """
         if not self._client:
-            return {"status": "disconnected"}
+            return {"status": "disconnected", "error": "Client not initialized"}
 
         try:
-            response = await self._client.get("/health")
-            if response.status_code == 200:
-                return {"status": "connected"}
-            return {"status": "error", "code": response.status_code}
+            async with self._get_client() as client:
+                # Try to list tools as a health check
+                tools = await client.list_tools()
+                return {
+                    "status": "connected",
+                    "tools_count": len(tools)
+                }
         except Exception as e:
-            return {"status": "disconnected", "error": str(e)}
+            return {
+                "status": "error",
+                "error": str(e)
+            }
 
 
 # Singleton instance
diff --git a/backend/pyproject.toml b/backend/pyproject.toml
index 7958984..5f5448c 100644
--- a/backend/pyproject.toml
+++ b/backend/pyproject.toml
@@ -17,6 +17,7 @@ dependencies = [
     "litellm>=1.50.0",
     "websockets>=12.0",
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diff --git a/backend/uv.lock b/backend/uv.lock
index 2711e0e..28d7465 100644
--- a/backend/uv.lock
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 version = "2026.1.4"
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