Make mcp_basic_agent cloud ready (#450)

* Make mcp_basic_agent cloud ready

* update mcp-basic-agent README for mcp-agent-cloud

* Readding agent to token summary and adding docstring

* Minor style fixes to the README

Author: andrew-lastmile

examples/basic/mcp_basic_agent/README.md

@@ -87,3 +87,67 @@ Run your MCP Agent app:
 ```bash
 uv run main.py
 ```
+
+## `4` [Beta] Deploy to the cloud
+
+### `a.` Log in to [MCP Agent Cloud](https://docs.mcp-agent.com/cloud/overview)
+
+```bash
+uv run mcp-agent login
+```
+
+### `b.` Update your `mcp_agent.secrets.yaml` to mark your developer secrets (keys)
+
+```yaml
+openai:
+  api_key: !developer_secret
+anthropic:
+  api_key: !developer_secret
+# Other secrets as needed
+```
+
+### `c.` Deploy your agent with a single command
+```bash
+uv run mcp-agent deploy my-first-agent
+```
+
+### `d.` Connect to your deployed agent as an MCP server through any MCP client
+
+#### Claude Desktop Integration
+
+Configure Claude Desktop to access your agent servers by updating your `~/.claude-desktop/config.json`:
+
+```json
+"my-agent-server": {
+  "command": "/path/to/npx",
+  "args": [
+    "mcp-remote",
+    "https://[your-agent-server-id].deployments.mcp-agent-cloud.lastmileai.dev/sse",
+    "--header",
+    "Authorization: Bearer ${BEARER_TOKEN}"
+  ],
+  "env": {
+        "BEARER_TOKEN": "your-mcp-agent-cloud-api-token"
+      }
+}
+```
+
+#### MCP Inspector
+
+Use MCP Inspector to explore and test your agent servers:
+
+```bash
+npx @modelcontextprotocol/inspector 
+```
+
+Make sure to fill out the following settings:
+
+| Setting | Value | 
+|---|---|
+| *Transport Type* | *SSE* |
+| *SSE* | *https://[your-agent-server-id].deployments.mcp-agent-cloud.lastmileai.dev/sse* |
+| *Header Name* | *Authorization* | 
+| *Bearer Token* | *your-mcp-agent-cloud-api-token* |
+
+> [!TIP]
+> In the Configuration, change the request timeout to a longer time period. Since your agents are making LLM calls, it is expected that it should take longer than simple API calls.

examples/basic/mcp_basic_agent/main.py

@@ -46,11 +46,18 @@
 # or loaded from mcp_agent.config.yaml/mcp_agent.secrets.yaml
 app = MCPApp(name="mcp_basic_agent")  # settings=settings)
 
-
-async def example_usage():
+@app.tool()
+async def example_usage()->str:
+    """
+    An example function/tool that uses an agent with access to the fetch and filesystem 
+    mcp servers. The agent will read the contents of mcp_agent.config.yaml, print the 
+    first 2 paragraphs of the mcp homepage, and summarize the paragraphs into a tweet. 
+    The example uses both OpenAI, Anthropic, and simulates a multi-turn conversation.
+    """
     async with app.run() as agent_app:
         logger = agent_app.logger
         context = agent_app.context
+        result = ""
 
         logger.info("Current config:", data=context.config.model_dump())
 
@@ -68,25 +75,26 @@ async def example_usage():
 
         async with finder_agent:
             logger.info("finder: Connected to server, calling list_tools...")
-            result = await finder_agent.list_tools()
-            logger.info("Tools available:", data=result.model_dump())
+            tools_list = await finder_agent.list_tools()
+            logger.info("Tools available:", data=tools_list.model_dump())
 
             llm = await finder_agent.attach_llm(OpenAIAugmentedLLM)
-            result = await llm.generate_str(
+            result += await llm.generate_str(
                 message="Print the contents of mcp_agent.config.yaml verbatim",
             )
             logger.info(f"mcp_agent.config.yaml contents: {result}")
 
             # Let's switch the same agent to a different LLM
             llm = await finder_agent.attach_llm(AnthropicAugmentedLLM)
 
-            result = await llm.generate_str(
+            result += await llm.generate_str(
                 message="Print the first 2 paragraphs of https://modelcontextprotocol.io/introduction",
             )
             logger.info(f"First 2 paragraphs of Model Context Protocol docs: {result}")
+            result += "\n\n"
 
             # Multi-turn conversations
-            result = await llm.generate_str(
+            result += await llm.generate_str(
                 message="Summarize those paragraphs in a 128 character tweet",
                 # You can configure advanced options by setting the request_params object
                 request_params=RequestParams(
@@ -101,8 +109,9 @@ async def example_usage():
             logger.info(f"Paragraph as a tweet: {result}")
 
         # Display final comprehensive token usage summary (use app convenience)
-        await display_token_summary(agent_app, finder_agent)
+        await display_token_summary(agent_app)
 
+    return result
 
 async def display_token_summary(app_ctx: MCPApp, agent: Agent | None = None):
     """Display comprehensive token usage summary using app/agent convenience APIs."""
@@ -129,6 +138,8 @@ async def display_token_summary(app_ctx: MCPApp, agent: Agent | None = None):
             )
             print(f"    Cost: ${data.cost:.4f}")
 
+    print("\n" + "=" * 50)
+
     # Optional: show a specific agent's aggregated usage
     if agent is not None:
         agent_usage = await agent.get_token_usage()