feat: add mcp demo

ai_agents/anthropic_mcp/README.md

@@ -0,0 +1,44 @@
+---
+title: Anthropic MCP Sample
+description: Simple usage of the Anthropic API via AutoKitteh
+integrations: ["anthropic", "slack"]
+categories: ["AI", "Productivity"]
+---
+
+# Anthropic MCP Sample
+
+[![Start with AutoKitteh](https://autokitteh.com/assets/autokitteh-badge.svg)](https://app.autokitteh.cloud/template?name=ai_agents/anthropic_mcp)
+
+This sample demonstrates how to interact with Anthropic's API using AutoKitteh, enabling you to leverage advanced language models for your automation workflows.
+
+It includes functionality to send prompts to Anthropic's Claude models and process the responses.
+
+API details:
+
+- [Anthropic API documentation](https://docs.anthropic.com/claude/docs)
+- [AutoKitteh Anthropic integration](https://docs.autokitteh.com/integrations/anthropic)
+
+## How It Works
+
+1. Sends a prompt to the Anthropic Claude API.
+2. Receives and logs the model's response in the AutoKitteh session.
+
+## Cloud Usage (Recommended)
+
+1. Initialize your Anthropic connection through the AutoKitteh UI.
+2. Copy the webhook trigger's URL (for the [Trigger Workflows](#trigger-workflows) section below):
+
+   - Hover over the trigger's (i) icon for the webhook you want to use.
+   - Click the copy icon next to the webhook URL for your selected trigger.
+   - (Detailed instructions
+     [here](https://docs.autokitteh.com/get_started/deployment#webhook-urls))
+
+3. Set any required environment variables or project variables (such as your Anthropic API key).
+
+## Trigger Workflows
+
+`send_prompt`:
+
+```shell
+curl -i "${WEBHOOK_URL}" --url-query prompt="Your question for Claude"
+```

ai_agents/anthropic_mcp/autokitteh.yaml

@@ -0,0 +1,24 @@
+# This file is part of the MCP project, which integrates with Anthropic and Slack.
+
+version: v1
+
+project:
+  name: anthropic_mcp
+
+  connections:
+    - name: slack_conn
+      integration: slack
+
+  triggers:
+    - name: send_msg
+      event_type: message
+      connection: slack_conn
+      call: program.py:start_workflow
+
+  vars:
+    - name: ANTHROPIC_API_KEY
+      value: ""
+    - name: SERVER_URL
+      value: ""
+    - name: SLACK_CHANNEL
+      value: ""

ai_agents/anthropic_mcp/program.py

@@ -0,0 +1,96 @@
+"""MCPClient integration with Anthropic and Slack.
+
+This module provides an MCPClient class that connects to a server.
+Processes queries using Anthropic's API, and posts results to a Slack channel.
+"""
+
+import asyncio
+from contextlib import AsyncExitStack
+import os
+
+from anthropic import Anthropic
+import autokitteh
+from autokitteh.slack import slack_client
+from mcp import ClientSession
+from mcp.client.streamable_http import streamablehttp_client
+
+
+slack = slack_client("slack_conn")
+
+ANTHROPIC_API_KEY = os.getenv("ANTHROPIC_API_KEY")
+SERVER_URL = os.getenv("SERVER_URL")
+SLACK_CHANNEL = os.getenv("SLACK_CHANNEL")  # ID or name.
+
+
+class MCPClient:
+    """Client for MCP server queries using Anthropic, with results posted to Slack."""
+
+    def __init__(self):
+        self.session: ClientSession | None = None
+        self.exit_stack = AsyncExitStack()
+        self.anthropic = Anthropic(api_key=ANTHROPIC_API_KEY)
+
+    @autokitteh.activity
+    async def connect_to_server(self, server_url: str):
+        transport = await self.exit_stack.enter_async_context(
+            streamablehttp_client(server_url)
+        )
+        self.read, self.write, _ = transport
+        self.session = await self.exit_stack.enter_async_context(
+            ClientSession(self.read, self.write)
+        )
+        await self.session.initialize()
+        print(f"\nConnected to server at {server_url}")
+
+    @autokitteh.activity
+    async def process_query(self, query: str) -> str:
+        messages = [{"role": "user", "content": query}]
+        tools = (await self.session.list_tools()).tools
+        available_tools = [
+            {
+                "name": t.name,
+                "description": t.description,
+                "input_schema": t.inputSchema,
+            }
+            for t in tools
+        ]
+
+        response = self.anthropic.messages.create(
+            model="claude-3-5-sonnet-20241022",
+            max_tokens=1000,
+            messages=messages,
+            tools=available_tools,
+        )
+
+        for content in response.content:
+            if content.type == "text":
+                slack.chat_postMessage(channel=SLACK_CHANNEL, text=content.text)
+            elif content.type == "tool_use":
+                result = await self.session.call_tool(content.name, content.input)
+                if result.content:
+                    text = f"tool result: {result.content[0].text}\n"
+                    slack.chat_postMessage(channel=SLACK_CHANNEL, text=text)
+                else:
+                    text = "Tool returned no content."
+                    slack.chat_postMessage(channel=SLACK_CHANNEL, text=text)
+
+    async def cleanup(self):
+        await self.exit_stack.aclose()
+
+
+async def handle_mcp_request(query):
+    """Handle a request to the MCP server with the given query."""
+    client = MCPClient()
+    try:
+        await client.connect_to_server(SERVER_URL)
+        await client.process_query(query)
+    except (ConnectionError, RuntimeError, ValueError) as e:
+        print(f"\nError: {str(e)}")
+    finally:
+        await client.cleanup()
+
+
+def start_workflow(event):
+    """Entrypoint for the MCPClient workflow."""
+    query = event.data.text
+    asyncio.run(handle_mcp_request(query))

tests/metadata_definitions.py

@@ -3,6 +3,7 @@
 METADATA = ("title", "description", "integrations", "categories")
 
 ALLOWED_INTEGRATIONS = (
+    "anthropic",
     "asana",
     "auth0",
     "autokitteh",