")
+ sys.exit(1)
+
+ client = MCPClient()
+ try:
+ await client.connect_to_server(sys.argv[1])
+ await client.chat_loop()
+ finally:
+ await client.cleanup()
+
+
+if __name__ == "__main__":
+ import sys
+
+ asyncio.run(main())
diff --git a/src/client_utils.py b/src/client_utils.py
new file mode 100644
index 0000000..98b518f
--- /dev/null
+++ b/src/client_utils.py
@@ -0,0 +1,275 @@
+from typing import Optional, Iterable, cast
+from contextlib import AsyncExitStack
+from mcp import ClientSession, StdioServerParameters, stdio_client
+
+from anthropic import Anthropic
+from anthropic.types import MessageParam, ToolParam
+
+from agents import Agent, Runner, gen_trace_id, trace
+from agents.mcp import MCPServerStdio
+from agents import ModelSettings
+
+_default_anthropic_model = "claude-3-7-sonnet-20250219"
+_anthropic_display_name = "Claude Sonnet"
+_openai_display_name = "OpenAI GPT-4o"
+
+
+class MCPClient:
+ def __init__(self):
+ # Initialize session and client objects
+ self.session: Optional[ClientSession] = None
+ self.exit_stack = AsyncExitStack()
+ self.anthropic = None
+ self.stdio = None
+ self.write = None
+ self.server_params = None
+ self.model_used = None
+ self.current_full_response = None
+
+ async def connect_to_server(self, server_script_path: str):
+ """Connect to an MCP server
+
+ Args:
+ server_script_path: Path to the server script (.py or .js)
+ """
+ if self.server_params is None:
+ self.server_params = {}
+
+ is_python = server_script_path.endswith(".py")
+ is_js = server_script_path.endswith(".js")
+ if not (is_python or is_js):
+ raise ValueError("Server script must be a .py or .js file")
+
+ command = "python" if is_python else "node"
+
+ self.server_params["command"] = command
+ self.server_params["args"] = [server_script_path]
+ server_params = StdioServerParameters(
+ command=command, args=[server_script_path], env=None
+ )
+
+ stdio_transport = await self.exit_stack.enter_async_context(
+ stdio_client(server_params)
+ )
+ self.stdio, self.write = stdio_transport
+ self.session = await self.exit_stack.enter_async_context(
+ ClientSession(self.stdio, self.write)
+ )
+
+ await self.session.initialize()
+
+ # List available tools
+ response = await self.session.list_tools()
+ tools = response.tools
+ print("\nConnected to server with tools:", [tool.name for tool in tools])
+
+ async def anthropic_creator(self, query, conversation_history):
+ if self.anthropic is None:
+ self.anthropic = Anthropic()
+
+ # Use provided conversation history or create new messages list
+ if conversation_history:
+ messages = conversation_history.copy()
+ else:
+ messages = []
+
+ # Add the user's current query
+ messages.append({"role": "user", "content": query})
+
+ response = await self.session.list_tools()
+ available_tools = [
+ {
+ "name": tool.name,
+ "description": tool.description,
+ "input_schema": tool.inputSchema,
+ }
+ for tool in response.tools
+ ]
+ self.model_used = _anthropic_display_name
+
+ return messages, available_tools
+
+ async def process_query_anthropic(
+ self,
+ query: str,
+ conversation_history=None,
+ max_tokens: int = 4096,
+ anthropic_model_used=None,
+ ) -> str:
+ """
+ Process a query using Claude and available tools with conversation history
+
+ Args:
+ query: Query to process
+ conversation_history: Optional list of previous messages
+ max_tokens: Maximum tokens for response (default: 4096)
+ anthropic_model_used: default model is claude-3-7-sonnet-20250219
+ """
+ model = anthropic_model_used or _default_anthropic_model
+ messages, available_tools = await self.anthropic_creator(
+ query, conversation_history
+ )
+
+ converted_messages = cast(Iterable[MessageParam], messages)
+ converted_available_tools = cast(Iterable[ToolParam], available_tools)
+
+ # Claude API call with conversation history
+ response = self.anthropic.messages.create(
+ model=model,
+ max_tokens=max_tokens,
+ messages=converted_messages,
+ tools=converted_available_tools,
+ )
+
+ # Process response and handle tool calls
+ final_text = []
+
+ for content in response.content:
+ if content.type == "text":
+ final_text.append(content.text)
+ elif content.type == "tool_use":
+ tool_name = content.name
+ tool_args = content.input
+ try:
+ tool_args = vars(tool_args)
+ except TypeError:
+ tool_args = tool_args
+
+ # Execute tool call
+ result = await self.session.call_tool(tool_name, tool_args)
+ final_text.append(f"[Calling tool {tool_name} with args {tool_args}]")
+
+ # Continue conversation with tool results
+ if hasattr(content, "text") and content.text:
+ messages.append({"role": "assistant", "content": content.text})
+ messages.append({"role": "user", "content": result.content})
+ converted_messages = cast(Iterable[MessageParam], messages)
+
+ # Get next response from Claude
+ response = self.anthropic.messages.create(
+ model=model,
+ max_tokens=max_tokens,
+ messages=converted_messages,
+ tools=converted_available_tools,
+ )
+
+ final_text.append(response.content[0].text)
+
+ return "\n".join(final_text)
+
+ async def chat_loop(self):
+ """Run an interactive chat loop with conversation history (max 5 messages)"""
+ conversation_history = []
+ max_history = 5 # Keep only the last 5 message pairs (10 messages total)
+
+ print("Interactive chat mode (type 'quit' to exit, 'clear' to reset history)")
+
+ while True:
+ try:
+ query = input("\nQuery: ").strip()
+
+ if query.lower() == "quit":
+ break
+ elif query.lower() == "clear":
+ conversation_history = []
+ print("Conversation history cleared")
+ continue
+
+ # Process the query with conversation history
+ response = await self.process_query_anthropic(
+ query, conversation_history=conversation_history
+ )
+ print("\n" + response)
+
+ # Update conversation history
+ conversation_history.append({"role": "user", "content": query})
+ conversation_history.append({"role": "assistant", "content": response})
+
+ # Trim history to keep only the most recent message pairs
+ if len(conversation_history) > max_history * 2:
+ conversation_history = conversation_history[-max_history * 2 :]
+
+ except Exception as e:
+ print(f"\nError: {str(e)}")
+
+ async def process_streaming_query(
+ self,
+ query: str,
+ conversation_history=None,
+ max_tokens: int = 4096,
+ anthropic_model_used=None,
+ ):
+ """
+ Process a query using Claude with streaming responses.
+
+ Args:
+ query: Query to process
+ conversation_history: Optional list of previous messages
+ max_tokens: Maximum tokens for response
+ anthropic_model_used: default model is claude-3-7-sonnet-20250219
+
+ Yields:
+ Chunks of text as they become available
+ """
+ model = anthropic_model_used or _default_anthropic_model
+ messages, available_tools = await self.anthropic_creator(
+ query, conversation_history
+ )
+ messages, available_tools = await self.anthropic_creator(
+ query, conversation_history
+ )
+
+ # Initialize the full response tracker
+ self.current_full_response = ""
+
+ def stream_chunks():
+ with self.anthropic.messages.stream(
+ model=model,
+ max_tokens=max_tokens,
+ messages=messages,
+ tools=available_tools,
+ ) as stream:
+ for event in stream:
+ if event.type == "content_block_delta":
+ if hasattr(event.delta, "text"):
+ yield event.delta.text
+ elif event.type == "tool_use":
+ yield f"\n[Tool requested: {event.name}]\n"
+
+ for chunk in stream_chunks():
+ self.current_full_response += chunk
+ yield chunk
+
+ async def cleanup(self):
+ """Clean up resources"""
+ await self.exit_stack.aclose()
+
+ async def process_query_with_openai_agent(
+ self, query: str, conversation_history=None
+ ):
+ if conversation_history:
+ messages = conversation_history.copy()
+ else:
+ messages = []
+ messages.append({"role": "user", "content": query})
+
+ async with MCPServerStdio(
+ name="StdioServer",
+ params=self.server_params,
+ client_session_timeout_seconds=300,
+ ) as server:
+ trace_id = gen_trace_id()
+ with trace(workflow_name="StdioServer", trace_id=trace_id):
+ print(
+ f"View trace: https://platform.openai.com/traces/trace?trace_id={trace_id}\n"
+ )
+ agent = Agent(
+ name="Assistant",
+ instructions="Respond to the question to the best of abilities. Use the appropriate tools to solve the problem, if any.",
+ mcp_servers=[server],
+ model_settings=ModelSettings(tool_choice="required"),
+ )
+ result = await Runner.run(starting_agent=agent, input=messages)
+ self.model_used = _openai_display_name
+
+ return result
diff --git a/src/server.py b/src/server.py
new file mode 100644
index 0000000..2674acc
--- /dev/null
+++ b/src/server.py
@@ -0,0 +1,421 @@
+import os
+import json
+from typing import Literal, Any
+
+import nats
+import asyncio
+from datetime import datetime
+from numpy import zeros
+
+from utils import ssl_config, NATS_SOURCE, NATS_PORT, MCP_PORT
+
+from mcp.server.fastmcp import FastMCP
+from litmussdk.devicehub import devices, tags
+from litmussdk.devicehub.drivers import driver_templates, DriverRecord
+from litmussdk.marketplace import list_all_containers, run_container
+from litmussdk.system import network, device_management
+from litmussdk.utils.env import update_env_variable
+
+
+# Create an MCP server
+mcp = FastMCP("LitmusMCPServer")
+mcp.settings.port = MCP_PORT
+
+
+@mcp.tool()
+def get_litmusedge_driver_list() -> list[str]:
+ """
+ Get List of Litmus Edge Drivers supported by Litmus MCP Server
+
+ Returns:
+ List of string driver names
+ """
+ list_drivers = [v for v in dir(driver_templates) if not v.startswith("_")]
+
+ return list_drivers
+
+
+@mcp.tool()
+def get_devicehub_devices() -> dict:
+ """
+ Retrieve all current DeviceHub devices configured on Litmus Edge.
+
+ Returns:
+ Dictionary of devices keyed by device name
+ """
+ output = {}
+ list_devices = devices.list_devices()
+ for current_device in list_devices:
+ output[current_device.name] = current_device.__dict__
+
+ return output
+
+
+@mcp.tool()
+def get_devicehub_device_tags(device_name: str) -> dict:
+ """
+ Retrieve all Tags of a single DeviceHub device configured on Litmus Edge.
+
+ Args:
+ device_name (str): Device name from where to grab tags
+
+ Returns:
+ Dictionary of tags keyed by tag name
+ """
+ output = {}
+ requested_device = None
+ list_devices = devices.list_devices()
+ for current_device in list_devices:
+ if device_name == current_device.name:
+ requested_device = current_device
+ if requested_device is None:
+ raise Exception(f"Device with name {device_name} not found")
+
+ list_tags = tags.list_registers_from_single_device(requested_device)
+ for current_tag in list_tags:
+ output[current_tag.tag_name] = current_tag.__dict__
+
+ return output
+
+
+@mcp.tool()
+def get_current_value_of_devicehub_tag(
+ device_name: str, tag_name: str | None, tag_id: str | None = None
+) -> dict | str:
+ """
+ Retrieve current value of a single DeviceHub device tag configured on Litmus Edge.
+
+ Args:
+ device_name:
+ tag_name: Tag name from where to grab tags
+ tag_id: (optional) Tag ID from where to grab tags
+
+ Returns:
+ Current value from raw topic
+ """
+ if not tag_name and not tag_id:
+ raise Exception("Either a Tag Name or Tag ID are required")
+
+ requested_device = None
+ list_devices = devices.list_devices()
+ for current_device in list_devices:
+ if device_name == current_device.name:
+ requested_device = current_device
+ if requested_device is None:
+ raise Exception(f"Device with name {device_name} not found")
+
+ list_tags = tags.list_registers_from_single_device(requested_device)
+ if tag_name:
+ requested_tag = next(
+ (tag for tag in list_tags if tag.tag_name == tag_name), None
+ )
+ else:
+ requested_tag = next((tag for tag in list_tags if tag.id == tag_id), None)
+
+ if requested_tag is None:
+ if tag_name:
+ raise Exception(f"Tag with name '{tag_name}' not found")
+ else:
+ raise Exception(f"Tag with ID '{tag_id}' not found")
+
+ requested_value_from_topic = next(
+ (topic.topic for topic in requested_tag.topics if topic.direction == "Output"),
+ "",
+ )
+ output = asyncio.run(get_current_value_on_topic(requested_value_from_topic))
+
+ return output
+
+
+@mcp.tool()
+def create_devicehub_device(
+ name: str,
+ selected_driver: str,
+):
+ """
+ Create a DeviceHub device on the connected Litmus Edge instance.
+
+ The DeviceHub module supports various protocols and manufacturers,
+ allowing register-based data polling via driver templates.
+
+ Args:
+ name: Name of the new device.
+ selected_driver: Driver template to use. Use `list_supported_drivers_on_edge` to view available options.
+ """
+ listed_driver = vars(driver_templates).get(selected_driver)
+ driver = DriverRecord.get(listed_driver.id)
+ properties = driver.get_default_properties()
+
+ device = devices.Device(name=name, properties=properties, driver=driver.id)
+ created_device = devices.create_device(device)
+ return created_device
+
+
+@mcp.tool()
+def update_environment_config(
+ key: Literal[
+ "EDGE_URL",
+ "EDGE_API_CLIENT_ID",
+ "EDGE_API_CLIENT_SECRET",
+ "VALIDATE_CERTIFICATE",
+ ],
+ value: str,
+) -> str:
+ """
+ Update Environment variables Config file for connecting to Litmus Edge
+
+ Args:
+ key (str): Keys such as EDGE_URL, EDGE_API_CLIENT_ID, EDGE_API_CLIENT_SECRET, VALIDATE_CERTIFICATE
+ value (str): Config value
+ """
+ update_env_variable(key=key, value=value)
+
+ return f"Config key {key} updated to {value}"
+
+
+@mcp.tool()
+def get_current_environment_config() -> dict:
+ """
+ Get the current environment configuration used for connecting to Litmus Edge.
+
+ Returns:
+ Dictionary of environment variable names and their values.
+ """
+ return {
+ "EDGE_URL": os.environ.get("EDGE_URL", ""),
+ "EDGE_API_CLIENT_ID": os.environ.get("EDGE_API_CLIENT_ID", ""),
+ "VALIDATE_CERTIFICATE": os.environ.get("VALIDATE_CERTIFICATE", ""),
+ }
+
+
+@mcp.tool()
+def get_litmusedge_friendly_name() -> str:
+ """
+ Get friendly name of LitmusEdge Device
+
+ Returns:
+ Device friendly name
+ """
+ return network.get_friendly_name()
+
+
+@mcp.tool()
+def set_litmusedge_friendly_name(new_friendly_name: str) -> None:
+ """
+ Change friendly name of LitmusEdge Device
+
+ Args:
+ new_friendly_name: New friendly name for the Device
+ """
+ return network.set_friendly_name(new_friendly_name)
+
+
+@mcp.tool()
+def get_cloud_activation_status() -> dict[str, Any]:
+ """
+ Get cloud activation status for the connection between Litmus Edge and Litmus Edge Manager
+
+ Returns:
+ Dictionary of cloud activation status
+ """
+ return device_management.show_cloud_registration_status()
+
+
+@mcp.tool()
+def get_all_containers_on_litmusedge() -> list[dict[str, Any]]:
+ """
+ List all containers in marketplace on Litmus Edge
+
+ Returns:
+ Array of all container/s details
+ """
+ return list_all_containers()
+
+
+@mcp.tool()
+def run_docker_container_on_litmusedge(docker_run_command: str) -> str:
+ """
+ Run a container in Litmus Edge marketplace with the docker command in the body.
+ This command runs on the litmus edge marketplace, not on the host system of the MCP server
+
+ Args:
+ docker_run_command: Docker run command to be run in string
+
+ Returns:
+ ID of container created
+ """
+ return run_container(docker_run_command)["id"]
+
+
+@mcp.tool()
+async def get_current_value_on_topic(
+ topic: str,
+ nats_source: str | None = None,
+ nats_port: str | None = None,
+) -> dict:
+ """
+ Subscribe to current value on a topic on Litmus Edge.
+ Change the global variable NATS_STATUS to "False" to end current subscription
+
+ Args:
+ topic: topic to subscribe to
+ nats_source: nats source, defaults to 10.30.50.1
+ nats_port: nats port, defaults to 4222
+
+ Returns:
+ Nats Subscription message
+ """
+ nats_source = nats_source or NATS_SOURCE
+ nats_port = nats_port or NATS_PORT
+
+ stop_event = asyncio.Event()
+
+ final_message = await nc_single_topic(nats_source, nats_port, topic, stop_event)
+ return final_message
+
+
+@mcp.tool()
+async def get_multiple_values_from_topic(
+ topic: str,
+ num_samples: int = 10,
+ nats_source: str | None = None,
+ nats_port: str | None = None,
+) -> object:
+ """
+ Get multiple values from a topic, for plotting or just returning a dictionary of value arrays
+
+ Args:
+ topic: NATS topic to subscribe to
+ num_samples: Number of messages to collect before plotting
+ nats_source: NATS source IP, defaults to 10.30.50.1
+ nats_port: NATS port, defaults to 4222
+
+ Returns:
+ Dictionary with timestamp and values as X and Y
+ """
+ nats_source = nats_source or NATS_SOURCE
+ nats_port = nats_port or NATS_PORT
+
+ stop_event = asyncio.Event()
+
+ output = await collect_multiple_values_from_topic(
+ nats_source, nats_port, topic, stop_event, num_samples
+ )
+
+ return output
+
+
+async def nc_single_topic(
+ nats_source: str,
+ nats_port: str,
+ nats_subscription_topic: str,
+ stop_event: asyncio.Event,
+) -> dict:
+ """
+ Subscribe to a single topic, and return a single message for the topic
+
+ Args:
+ nats_source: NATS source IP, defaults to 10.30.50.1
+ nats_port: NATS port, defaults to 4222
+ nats_subscription_topic: NATS topic to subscribe to
+ stop_event: Asyncio Event to stop collecting values
+
+ Returns:
+ Single message from the subscribed topic
+ """
+ ssl_context = ssl_config()
+ nc = await nats.connect(f"nats://{nats_source}:{nats_port}", tls=ssl_context)
+
+ result_message = {}
+
+ async def message_handler(msg):
+ nonlocal result_message
+ if result_message:
+ stop_event.set()
+
+ data = msg.data.decode()
+ message = json.loads(data)
+ result_message = message
+
+ await nc.subscribe(nats_subscription_topic, cb=message_handler)
+ await stop_event.wait()
+ await nc.drain()
+
+ return result_message
+
+
+async def collect_multiple_values_from_topic(
+ nats_source: str,
+ nats_port: str,
+ topic: str,
+ stop_event: asyncio.Event,
+ num_samples: int = 10,
+) -> object:
+ """
+ Collect multiple values from a topic, for plotting or just returning a dictionary
+
+ Args:
+ nats_source: NATS source IP, defaults to 10.30.50.1
+ nats_port: NATS port, defaults to 4222
+ topic: NATS topic to subscribe to
+ stop_event: Asyncio Event to stop collecting values
+ num_samples: Number of messages to collect
+
+ Returns:
+ Dict of results
+ """
+ ssl_context = ssl_config()
+ nc = await nats.connect(f"nats://{nats_source}:{nats_port}", tls=ssl_context)
+
+ results = {
+ # "timestamps": zeros(num_samples),
+ "humanTimestamps": ["" for _ in range(num_samples)],
+ "values": zeros(num_samples),
+ }
+ counter = 0
+
+ async def message_handler(msg):
+ nonlocal counter, results
+ data = msg.data.decode()
+ payload = json.loads(data)
+
+ value = payload["value"]
+ timestamp = payload["timestamp"]
+ human_ts = str(datetime.fromtimestamp(timestamp / 1000))
+
+ if counter < num_samples:
+ # results["timestamps"][counter] = timestamp
+ results["values"][counter] = value
+ results["humanTimestamps"][counter] = human_ts
+ counter += 1
+ else:
+ stop_event.set()
+ # # Sliding window
+ # if config["counter"] < num_samples:
+ # i = config["counter"]
+ # results["values"][i] = value
+ # results["timestamps"][i] = timestamp
+ # results["humanTimestamps"][i] = human_ts
+ # config["counter"] += 1
+ # else:
+ # config["counter"] = 0
+ # results["values"][:-1] = results["values"][1:]
+ # results["timestamps"][:-1] = results["timestamps"][1:]
+ # results["humanTimestamps"][:-1] = results["humanTimestamps"][1:]
+ #
+ # results["values"][-1] = value
+ # results["timestamps"][-1] = timestamp
+ # results["humanTimestamps"][-1] = human_ts
+ #
+ # if config["counter"] >= num_samples:
+ # stop_event.set()
+
+ await nc.subscribe(topic, cb=message_handler)
+ await stop_event.wait()
+ await nc.drain()
+
+ return results
+
+
+if __name__ == "__main__":
+ mcp.run(transport="stdio")
diff --git a/src/utils.py b/src/utils.py
new file mode 100644
index 0000000..09bcdff
--- /dev/null
+++ b/src/utils.py
@@ -0,0 +1,201 @@
+import ssl
+import os
+import logging
+from typing import List, Dict, Any, Tuple
+import dotenv
+
+NATS_SOURCE = "10.30.50.1"
+NATS_PORT = "4222"
+MCP_PORT = 8000
+
+BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+JINJA_TEMPLATE_DIR = os.path.join(BASE_DIR, "templates")
+STATIC_DIR = os.path.join(BASE_DIR, "static")
+
+
+EDGE_URL = ""
+EDGE_API_CLIENT_ID = ""
+EDGE_API_CLIENT_SECRET = ""
+VALIDATE_CERTIFICATE = ""
+ANTHROPIC_KEY = ""
+
+_key_EDGE_URL = "EDGE_URL"
+_key_EDGE_API_CLIENT_ID = "EDGE_API_CLIENT_ID"
+_key_EDGE_API_CLIENT_SECRET = "EDGE_API_CLIENT_SECRET"
+_key_VALIDATE_CERTIFICATE = "VALIDATE_CERTIFICATE"
+_default_value_VALIDATE_CERTIFICATE = "false"
+
+key_of_anthropic_api_key = "ANTHROPIC_API_KEY"
+key_of_openai_api_key = "OPENAI_API_KEY"
+MODEL_NAME_OPENAI = "openai"
+MODEL_NAME_ANTHROPIC = "anthropic"
+MODEL_PREFERENCE = "PREFERRED_MODEL"
+
+# Maximum number of message pairs to keep in history
+MAX_HISTORY_PAIRS = 5
+
+# Store conversation history in memory (this will be reset if the server restarts)
+CONVERSATION_HISTORY = []
+STREAMING_ALLOWED = True
+
+
+def ssl_config():
+ ssl_ctx = ssl.create_default_context()
+ ssl_ctx.check_hostname = False
+ ssl_ctx.verify_mode = ssl.CERT_NONE
+ return ssl_ctx
+
+
+def mcp_env_loader():
+ global EDGE_URL, EDGE_API_CLIENT_ID, EDGE_API_CLIENT_SECRET, VALIDATE_CERTIFICATE, ANTHROPIC_KEY
+
+ # Load .env file if available
+ dotenv_path = dotenv.find_dotenv()
+ if dotenv_path:
+ dotenv.load_dotenv(dotenv_path, override=True)
+ else:
+ logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
+ logging.info(
+ "Did not find .env file in current working directory. Defaulting to system variables"
+ )
+
+ EDGE_URL = os.environ.get(_key_EDGE_URL, "")
+ EDGE_API_CLIENT_ID = os.environ.get(_key_EDGE_API_CLIENT_ID, "")
+ EDGE_API_CLIENT_SECRET = os.environ.get(_key_EDGE_API_CLIENT_SECRET, "")
+ VALIDATE_CERTIFICATE = os.environ.get(
+ _key_VALIDATE_CERTIFICATE, _default_value_VALIDATE_CERTIFICATE
+ )
+ ANTHROPIC_KEY = os.environ.get(key_of_anthropic_api_key, "")
+
+
+def _get_env_vars(env_file, override):
+ path_to_env = dotenv.find_dotenv(env_file)
+ if path_to_env == "" or None:
+ with open(".env", "w") as f:
+ f.write("ENV=Initiate")
+ path_to_env = dotenv.find_dotenv(env_file)
+ dotenv.load_dotenv(path_to_env or env_file, override=override)
+ env_vars = {}
+
+ if os.path.exists(path_to_env):
+ with open(path_to_env, "r") as file:
+ for line in file:
+ if "=" in line:
+ k, v = line.strip().split("=", 1)
+ env_vars[k] = v
+ return env_vars, path_to_env
+
+
+def get_current_mcp_env(env_file: str = ".env"):
+ return _get_env_vars(env_file, override=False)
+
+
+def mcp_env_remover(key: str, env_file: str = ".env"):
+ env_vars, path_to_env = _get_env_vars(env_file, override=True)
+
+ env_vars.pop(key, None)
+ with open(path_to_env, "w") as file:
+ for k, v in env_vars.items():
+ file.write(f"{k}={v}\n")
+
+ print(f"Removed {key} in {env_file}")
+
+
+def mcp_env_updater(key: str, value: str | bool, env_file: str = ".env"):
+ env_vars, path_to_env = _get_env_vars(env_file, override=True)
+ env_vars[key] = value
+
+ # Write updated content back to .env
+ with open(path_to_env, "w") as file:
+ for k, v in env_vars.items():
+ file.write(f"{k}={v}\n")
+
+ print(f"Updated {key} in {env_file}")
+
+
+def check_model_key() -> Tuple[bool, str]:
+ anthropic_exists = os.environ.get(key_of_anthropic_api_key)
+ openai_exists = os.environ.get(key_of_openai_api_key)
+ preferred_model = os.environ.get(MODEL_PREFERENCE)
+
+ if preferred_model is not None and preferred_model in [
+ MODEL_NAME_OPENAI,
+ MODEL_NAME_ANTHROPIC,
+ ]:
+ return True, preferred_model
+
+ if anthropic_exists is None and openai_exists is None:
+ return False, ""
+
+ if openai_exists:
+ mcp_env_updater(MODEL_PREFERENCE, MODEL_NAME_OPENAI)
+ return True, MODEL_NAME_OPENAI
+
+ mcp_env_updater(MODEL_PREFERENCE, MODEL_NAME_ANTHROPIC)
+ return True, MODEL_NAME_ANTHROPIC
+
+
+def get_conversation_history() -> List[Dict[str, Any]]:
+ """Get global conversation history"""
+ global CONVERSATION_HISTORY
+ return CONVERSATION_HISTORY
+
+
+def update_conversation_history(query: str | None, response: str | None, clear=False):
+ """Update the global conversation history"""
+ global CONVERSATION_HISTORY
+ if clear:
+ CONVERSATION_HISTORY = []
+ return
+
+ # Add new messages
+ CONVERSATION_HISTORY.append({"role": "user", "content": query})
+ CONVERSATION_HISTORY.append({"role": "assistant", "content": response})
+
+ # Trim to keep only MAX_HISTORY_PAIRS
+ if len(CONVERSATION_HISTORY) > MAX_HISTORY_PAIRS * 2:
+ CONVERSATION_HISTORY = CONVERSATION_HISTORY[-MAX_HISTORY_PAIRS * 2 :]
+
+
+def get_chat_log(conversation_history) -> List[Dict[str, Any]]:
+ """Get chat log"""
+ chat_log = []
+
+ # Format conversation history for display
+ for i in range(0, len(conversation_history), 2):
+ if i + 1 < len(conversation_history):
+ chat_log.append(
+ {
+ "user": conversation_history[i]["content"],
+ "assistant": conversation_history[i + 1]["content"],
+ }
+ )
+ return chat_log
+
+
+def check_streaming_status(current_route: str) -> Tuple[bool, str]:
+ redirect = False
+ new_route = current_route
+
+ if STREAMING_ALLOWED and current_route == "/":
+ redirect = True
+ new_route = "/streaming"
+ return redirect, new_route
+ if not STREAMING_ALLOWED and current_route == "/streaming":
+ redirect = True
+ new_route = "/"
+ return redirect, new_route
+
+ return redirect, new_route
+
+
+def markdown_to_html(text):
+ if not text:
+ return ""
+ return text.replace("\n", "
+
+
+
+
+
diff --git a/static/litmus-logo-light.svg b/static/litmus-logo-light.svg
new file mode 100644
index 0000000..b650f6f
--- /dev/null
+++ b/static/litmus-logo-light.svg
@@ -0,0 +1,9 @@
+
+
+
+
+
+
diff --git a/static/query_script.js b/static/query_script.js
new file mode 100644
index 0000000..6c9a064
--- /dev/null
+++ b/static/query_script.js
@@ -0,0 +1,59 @@
+document.addEventListener("DOMContentLoaded", () => {
+ const form = document.getElementById("query-form");
+ const textarea = document.getElementById("query");
+ const loading = document.getElementById("loading");
+ const submitButton = form?.querySelector("button[type='submit']");
+ const clearHistoryForm = document.getElementById("clear-history-form");
+
+ // Add a confirmation for clearing history
+ if (clearHistoryForm) {
+ clearHistoryForm.addEventListener("submit", function (e) {
+ if (!confirm("Are you sure you want to clear all chat history?")) {
+ e.preventDefault();
+ }
+ });
+ }
+
+ if (!form || !textarea || !loading || !submitButton) {
+ console.warn("Essential elements for non-streaming form not found");
+ return;
+ }
+
+ // Auto-resize textarea
+ function resizeTextarea() {
+ textarea.style.height = "auto";
+ textarea.style.height = `${textarea.scrollHeight}px`;
+ }
+
+ resizeTextarea();
+ textarea.addEventListener("input", resizeTextarea);
+
+ let isSubmitting = false;
+
+ function handlePreSubmit() {
+ const query = textarea.value.trim();
+ if (!query || isSubmitting) return false;
+
+ isSubmitting = true;
+ submitButton.disabled = true; // ✅ Disable only button
+ loading.style.display = "flex";
+ return true;
+ }
+
+ // Handle Enter key submission
+ textarea.addEventListener("keydown", function (e) {
+ if (e.key === "Enter" && !e.shiftKey) {
+ e.preventDefault();
+ if (handlePreSubmit()) {
+ form.requestSubmit(); // Will POST and reload
+ }
+ }
+ });
+
+ // Handle manual submit click
+ form.addEventListener("submit", function () {
+ if (!isSubmitting) {
+ handlePreSubmit();
+ }
+ });
+});
diff --git a/static/query_streaming_script.js b/static/query_streaming_script.js
new file mode 100644
index 0000000..bd83fcb
--- /dev/null
+++ b/static/query_streaming_script.js
@@ -0,0 +1,164 @@
+document.addEventListener("DOMContentLoaded", () => {
+ const form = document.getElementById("query-form");
+ if (!form) return;
+
+ const textarea = document.getElementById("query");
+ const loading = document.getElementById("loading");
+ const userQuery = document.getElementById("user-query");
+ const responseStream = document.getElementById("response-stream");
+ const currentQueryContainer = document.getElementById("current-query-container");
+ const currentResponseContainer = document.getElementById("current-response-container");
+ const submitButton = form.querySelector("button[type='submit']");
+ const clearHistoryForm = document.getElementById("clear-stream-history-form");
+
+ // Add a confirmation for clearing history
+ if (clearHistoryForm) {
+ clearHistoryForm.addEventListener("submit", function(e) {
+ if (!confirm("Are you sure you want to clear all chat history?")) {
+ e.preventDefault();
+ }
+ });
+ }
+
+ if (!textarea || !loading || !userQuery || !responseStream ||
+ !currentQueryContainer || !currentResponseContainer || !submitButton) {
+ console.error("Required streaming mode elements not found");
+ return;
+ }
+
+ let isSubmitting = false;
+
+ function resizeTextarea() {
+ textarea.style.height = "auto";
+ textarea.style.height = `${textarea.scrollHeight}px`;
+ }
+
+ resizeTextarea();
+ textarea.addEventListener("input", resizeTextarea);
+
+ let cursorInterval;
+
+ function stopCursor() {
+ if (cursorInterval) {
+ clearInterval(cursorInterval);
+ cursorInterval = null;
+ if (responseStream.textContent.endsWith("▍")) {
+ responseStream.textContent = responseStream.textContent.slice(0, -1);
+ }
+ }
+ }
+
+ function startCursor() {
+ stopCursor();
+ cursorInterval = setInterval(() => {
+ if (responseStream.textContent.endsWith("▍")) {
+ responseStream.textContent = responseStream.textContent.slice(0, -1);
+ } else {
+ responseStream.textContent += "▍";
+ }
+ window.scrollTo(0, document.body.scrollHeight);
+ }, 500);
+ }
+
+ async function submitQuery() {
+ const query = textarea.value.trim();
+ if (!query || isSubmitting) return;
+
+ try {
+ isSubmitting = true;
+
+ loading.style.display = "flex";
+ textarea.disabled = true;
+ submitButton.disabled = true;
+
+ userQuery.textContent = query;
+ responseStream.textContent = "";
+ currentQueryContainer.style.display = "block";
+ currentResponseContainer.style.display = "block";
+
+ const formData = new FormData();
+ formData.append("query", query);
+
+ const xhr = new XMLHttpRequest();
+ xhr.open("POST", "/streaming", true);
+
+ let responseText = "";
+ let lastProcessedLength = 0;
+
+ xhr.onprogress = function () {
+ if (xhr.status === 200) {
+ const newChunk = xhr.responseText.substring(lastProcessedLength);
+ lastProcessedLength = xhr.responseText.length;
+ if (newChunk) {
+ stopCursor();
+ responseStream.textContent += newChunk;
+ startCursor();
+ responseText += newChunk;
+ window.scrollTo(0, document.body.scrollHeight);
+ }
+ }
+ };
+
+ xhr.onload = function () {
+ if (xhr.status === 200) {
+ const finalChunk = xhr.responseText.substring(lastProcessedLength);
+ if (finalChunk) {
+ stopCursor();
+ responseStream.textContent += finalChunk;
+ }
+ } else {
+ console.error("Request failed with status:", xhr.status);
+ responseStream.textContent = `Error ${xhr.status}: Request failed`;
+ }
+ finishSubmission();
+ };
+
+ xhr.onerror = function () {
+ console.error("Network error occurred");
+ responseStream.textContent = "Network error occurred. Please try again.";
+ finishSubmission();
+ };
+
+ xhr.ontimeout = function () {
+ console.error("Request timed out");
+ responseStream.textContent = "Request timed out. Please try again.";
+ finishSubmission();
+ };
+
+ xhr.send(formData);
+ startCursor();
+
+ } catch (error) {
+ console.error("Error submitting query:", error);
+ responseStream.textContent = `Error: ${error.message}`;
+ finishSubmission();
+ }
+ }
+
+ function finishSubmission() {
+ stopCursor();
+ loading.style.display = "none";
+ textarea.disabled = false;
+ submitButton.disabled = false;
+ textarea.value = "";
+ resizeTextarea();
+ textarea.focus();
+ isSubmitting = false;
+ window.scrollTo(0, document.body.scrollHeight);
+ }
+
+ form.addEventListener("submit", function (e) {
+ e.preventDefault();
+ submitQuery();
+ });
+
+ textarea.addEventListener("keydown", function (e) {
+ if (e.key === "Enter" && !e.shiftKey) {
+ e.preventDefault();
+ submitQuery();
+ } else if (e.key === "Enter" && (e.ctrlKey || e.metaKey)) {
+ e.preventDefault();
+ submitQuery();
+ }
+ });
+});
diff --git a/static/style.css b/static/style.css
new file mode 100644
index 0000000..3bda627
--- /dev/null
+++ b/static/style.css
@@ -0,0 +1,360 @@
+hr {
+ border: 0;
+ height: 1px;
+ background-image: linear-gradient(to right, rgba(0, 0, 0, 0), rgba(0, 0, 0, 0.75), rgba(0, 0, 0, 0));
+}
+h1 {
+ margin-bottom: 24px;
+ font-weight: 600;
+}
+
+body {
+ background-color: #f2f8f6;
+ font-family: 'Space Grotesk', sans-serif;
+ margin: 0;
+ padding: 0;
+ display: flex;
+ align-items: center;
+ justify-content: center;
+ min-height: 100vh;
+ min-width: 700px;
+ color: #002f2b;
+}
+/* Top nav*/
+.top-nav {
+ width: 100%;
+ display: inline-flex;
+ background: transparent;
+ overflow: hidden;
+}
+.top-nav a{
+ color: #002f2b;
+ border-radius: 8px;
+ font-size: 16px;
+ text-decoration: none;
+ display: inline-flex;
+ align-items: center;
+ justify-content: center;
+ transition: background-color 0.2s ease, box-shadow 0.2s ease;
+ cursor: pointer;
+ padding: 8px 10px;
+}
+.top-nav a:hover {
+ background-color: #e6fbf3;
+ box-shadow: 0 1px 4px rgba(0, 0, 0, 0.05);
+}
+.top-nav a:focus {
+ outline: none;
+ box-shadow: 0 0 0 2px rgba(61, 219, 144, 0.2);
+}
+
+/*Body Container*/
+.container {
+ background: white;
+ padding: 40px;
+ border-radius: 12px;
+ box-shadow: 0 4px 10px rgba(0, 0, 0, 0.05);
+ width: 100%;
+ max-width: 700px;
+ box-sizing: border-box;
+ text-align: left;
+}
+
+/*Input form field*/
+input[type="text"] {
+ width: 100%;
+ padding: 14px;
+ font-size: 14px;
+ border: 1px solid #ccc;
+ border-radius: 8px;
+ margin-bottom: 20px;
+ box-sizing: border-box;
+ font-family: 'Helvetica', 'Arial', sans-serif;
+ box-shadow: 0 4px 10px rgba(0, 0, 0, 0.08);
+}
+textarea[name="query"] {
+ width: 100%;
+ min-height: 44px;
+ padding: 14px;
+ font-size: 16px;
+ font-family: 'Helvetica', 'Arial', sans-serif;
+ border: 1px solid #ccc;
+ border-radius: 8px;
+ margin-bottom: 20px;
+ box-sizing: border-box;
+ box-shadow: 0 4px 10px rgba(0, 0, 0, 0.08);
+ resize: none;
+ overflow-y: hidden;
+ line-height: 1.4;
+ transition: height 0.15s ease-in-out;
+}
+textarea + small {
+ text-align: right;
+ display: block;
+ margin-top: -14px;
+ margin-bottom: 16px;
+}
+
+
+/*Button*/
+button {
+ background-color: #4fb896;
+ color: white;
+ border: none;
+ padding: 12px 24px;
+ font-size: 16px;
+ font-family: 'Space Grotesk', sans-serif;
+ font-weight: 500;
+ border-radius: 8px;
+ cursor: pointer;
+ transition: background-color 0.2s ease, box-shadow 0.2s ease;
+ box-shadow: 0 2px 4px rgba(0, 0, 0, 0.08);
+ height: fit-content;
+}
+button:hover {
+ background-color: #70c7a9;
+ box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
+}
+button:focus {
+ outline: none;
+ box-shadow: 0 0 0 2px rgba(79, 184, 150, 0.3);
+}
+
+.action-buttons {
+ display: flex;
+ gap: 10px;
+}
+.side-button {
+ background-color: #f2f8f6;
+ color: #002f2b;
+ font-size: 14px;
+ padding: 8px 12px;
+ border-radius: 8px;
+ text-decoration: none;
+ display: inline-flex;
+ align-items: center;
+ justify-content: center;
+ border: 1px solid #dce8e2;
+ transition: background-color 0.2s ease, box-shadow 0.2s ease;
+ cursor: pointer;
+}
+.side-button:hover {
+ background-color: #e6fbf3;
+ box-shadow: 0 1px 4px rgba(0, 0, 0, 0.05);
+}
+.side-button:focus {
+ outline: none;
+ box-shadow: 0 0 0 2px rgba(61, 219, 144, 0.2);
+}
+select.select-button {
+ background-color: #f2f8f6;
+ color: #002f2b;
+ font-size: 14px;
+ padding: 8px 12px;
+ border-radius: 8px;
+ border: 1px solid #dce8e2;
+ font-family: 'Space Grotesk', sans-serif;
+ cursor: pointer;
+ box-shadow: 0 1px 2px rgba(0, 0, 0, 0.03);
+ height: 40px;
+ line-height: 1.4;
+}
+select.select-button:hover {
+ background-color: #e6fbf3;
+}
+select.select-button:focus {
+ outline: none;
+ box-shadow: 0 0 0 2px rgba(61, 219, 144, 0.2);
+}
+
+
+.md-icon-btn {
+ background-color: transparent;
+ padding: 6px;
+ border: none;
+ border-radius: 50%;
+ cursor: pointer;
+ display: inline-flex;
+ align-items: center;
+ justify-content: center;
+ transition: background-color 0.2s ease;
+ width: 40px;
+ height: 40px;
+ font-size: 0;
+}
+.md-icon-btn svg {
+ fill: #e04848;
+ transition: fill 0.2s ease;
+ width: 20px;
+ height: 20px;
+}
+.md-icon-btn:hover {
+ background-color: rgba(224, 72, 72, 0.1);
+}
+.md-icon-btn:hover svg {
+ fill: #b43d3d;
+}
+.md-icon-btn:focus {
+ outline: none;
+ box-shadow: 0 0 0 2px rgba(224, 72, 72, 0.3);
+}
+
+/*Loading animation and text*/
+.loading {
+ display: none;
+ align-items: center;
+ font-style: normal;
+ color: #278a6b;
+ font-weight: 500;
+ font-size: 18px;
+}
+
+.loading .dot-pulse {
+ display: flex;
+ gap: 4px;
+ margin-right: 8px;
+}
+
+.loading .dot-pulse div {
+ width: 10px;
+ height: 10px;
+ background-color: #278a6b;
+ border-radius: 50%;
+ animation: pulse 0.9s infinite ease-in-out;
+ opacity: 1;
+ transform: scale(1);
+}
+
+.loading .dot-pulse div:nth-child(2) {
+ animation-delay: 0.15s;
+}
+
+.loading .dot-pulse div:nth-child(3) {
+ animation-delay: 0.3s;
+}
+
+@keyframes pulse {
+ 0%, 80%, 100% {
+ transform: scale(0.95);
+ opacity: 0.85;
+ }
+ 40% {
+ transform: scale(1.4);
+ opacity: 1;
+ }
+}
+
+@keyframes pulse {
+ 0%, 80%, 100% {
+ transform: scale(0.9);
+ opacity: 0.6;
+ }
+ 40% {
+ transform: scale(1.2);
+ opacity: 1;
+ }
+}
+
+/*Response part*/
+.query-label {
+ font-weight: 600;
+ margin-top: 30px;
+}
+
+.response {
+ margin-top: 12px;
+ background: #f2f8f6;
+ padding: 20px;
+ border-radius: 8px;
+ border: 1px solid #dce8e2;
+ white-space: pre-wrap;
+ word-wrap: break-word;
+}
+
+/*Chat from user and mcp response*/
+.chat-log {
+ margin-top: 30px;
+ display: flex;
+ flex-direction: column;
+ gap: 16px;
+}
+
+.chat-message {
+ padding: 16px;
+ border-radius: 12px;
+ box-sizing: border-box;
+}
+
+.chat-message.user {
+ background-color: #edfdf6;
+ border: 1px solid #88eabb;
+ color: #002f2b;
+}
+
+.chat-message.mcp {
+ background-color: #f1f0ff;
+ border: 1px solid #c2c1fb;
+ color: #032125;
+}
+
+.chat-message .label {
+ font-weight: 600;
+ color: #002f2b;
+ margin-bottom: 6px;
+}
+
+.chat-message .message {
+ white-space: pre-wrap;
+ word-break: break-word;
+ line-height: 1.6;
+}
+
+.form-actions {
+ display: flex;
+ align-items: center;
+ gap: 12px;
+ margin-top: 10px;
+}
+
+/*Update Config*/
+.toast {
+ background-color: #e6fbf3;
+ border: 1px solid #3adb90;
+ color: #002f2b;
+ padding: 10px 14px;
+ font-size: 14px;
+ font-weight: 500;
+ border-radius: 6px;
+ margin-bottom: 16px;
+ opacity: 1;
+ transition: opacity 0.6s ease;
+}
+
+/*Update config and get env variables*/
+.env-form {
+ display: flex;
+ gap: 8px;
+ align-items: center;
+}
+.env-form button{
+ margin-bottom: auto;
+}
+.env-table {
+ width: 100%;
+ max-width: 100%;
+ table-layout: fixed;
+ border-collapse: collapse;
+ font-size: 14px;
+ word-break: break-word;
+}
+.env-table th,
+.env-table td {
+ padding: 8px 12px;
+ border-bottom: 1px solid #e0e0e0;
+ text-align: left;
+}
+
+.env-table th {
+ font-weight: 600;
+ background-color: #f2f8f6;
+}
\ No newline at end of file
diff --git a/templates/500.html b/templates/500.html
new file mode 100644
index 0000000..38a5fa7
--- /dev/null
+++ b/templates/500.html
@@ -0,0 +1,19 @@
+{% extends "base.html" %}
+
+{% block scripts %}
+
+{% endblock %}
+
+{% block title %}Server Error · Litmus MCP Server{% endblock %}
+
+{% block content %}
+
+
Litmus MCP Server 🧠
+
+
+
+
500 - Internal Server Error
+
Oops! Something went wrong on our end, or by the API call timeouts.
+
Home
+
{% block title %}Litmus MCP Server 🧠{% endblock %}
+
+
+ {% block content %}{% endblock %}
+
+
Litmus MCP Server 🧠
+
+
+
+
Health Status 🩺
+
Status: {{ status }}
+
Version: {{ version }}
+
+
+
Litmus MCP Server 🧠
+
+ {% if has_history %}
+
+ {% endif %}
+
+
+ {% for exchange in chat_log | reverse %} {# Reverse chat #}
+
+
You:
+
{{ exchange.user | markdown_to_html | safe }}
+
+
+
MCP Server: (Model: {{ exchange.model | safe }})
+
{{ exchange.assistant | markdown_to_html | safe }}
+
+ {% endfor %}
+
+
Litmus MCP Server 🧠
+
+ {% if has_history %}
+
+ {% endif %}
+
+
+ ⚠️ Streaming response is currently in beta , and only supported with Anthropic Models .
+
+
+
+
+
+ {% for exchange in chat_log %}
+
+
You:
+
{{ exchange.user | safe }}
+
+
+
MCP Server: (Model: {{ exchange.model | safe }})
+
{{ exchange.assistant | safe }}
+
+ {% endfor %}
+
+
+
+
+
+{% endblock %}
+
+{% block scripts %}
+
+
+{% endblock %}
\ No newline at end of file
diff --git a/templates/setup.html b/templates/setup.html
new file mode 100644
index 0000000..651b636
--- /dev/null
+++ b/templates/setup.html
@@ -0,0 +1,33 @@
+{% extends "base.html" %}
+
+{% block title %}Setup · Litmus MCP Server{% endblock %}
+
+{% block content %}
+Setup Litmus MCP Server 🔑
+Please enter either your Anthropic API key, and/or OPEN AI API Key to start/update the server.
+
+
Update Config Variables ⚙️
+
+
+
+
+
+
+
+{% if updated %}
+✅ Environment variable updated.
+
+{% endif %}
+
+Current Environment
+
+
+
+
+
+ Key
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+ {{ key }}
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+
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