Add additional MCP examples (#430)

# Motivation

<!-- Why is this change necessary? -->

# Content

<!-- Please include a summary of the change -->

# Testing

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# Please check the following before marking your PR as ready for review

- [ ] I have added tests for my changes
- [ ] I have updated the documentation or added new documentation as
needed

---------

Co-authored-by: rushilpatel0 <[email protected]>

Author: rushilpatel0

docs/introduction/ide-usage.mdx

@@ -59,8 +59,8 @@ it will allow an agent to:
 - improve a codemod
 - get setup instructions
 
-### Configuration
-#### Usage with Cline:
+### IDE Configuration
+#### Cline
 Add this to your cline_mcp_settings.json:
 ```
 {
@@ -79,7 +79,7 @@ Add this to your cline_mcp_settings.json:
 ```
 
 
-#### Usage with Cursor:
+#### Cursor:
 Under the `Settings` > `Feature` > `MCP Servers` section, click "Add New MCP Server" and add the following:
 
 ```

docs/mint.json

@@ -100,7 +100,8 @@
 				"tutorials/sqlalchemy-1.6-to-2.0",
 				"tutorials/fixing-import-loops-in-pytorch",
 				"tutorials/python2-to-python3",
-				"tutorials/flask-to-fastapi"
+				"tutorials/flask-to-fastapi",
+				"tutorials/build-mcp"
 			]
 		},
 		{

docs/tutorials/build-mcp.mdx

@@ -0,0 +1,75 @@
+---
+title: "Building a Model Context Protocol server with Codegen"
+sidebarTitle: "MCP Server"
+icon: "boxes-stacked"
+iconType: "solid"
+---
+
+Learn how to build a Model Context Protocol (MCP) server that enables AI models to understand and manipulate code using Codegen's powerful tools.
+
+This guide will walk you through creating an MCP server that can provide semantic code search
+
+<Info>View the full code in our [examples repository](https://github.com/codegen-sh/codegen-sdk/tree/develop/src/codegen/extensions/mcp)</Info>
+
+
+## Setup:
+Install the MCP python library
+```
+uv pip install mcp
+```
+
+## Step 1: Setting Up Your MCP Server
+
+First, let's create a basic MCP server using Codegen's MCP tools:
+
+server.py
+```python
+from codegen import Codebase
+from mcp.server.fastmcp import FastMCP
+from typing import Annotated
+# Initialize the codebase
+codebase = Codebase.from_repo(".")
+
+# create the MCP server using FastMCP
+mcp = FastMCP(name="demo-mcp", instructions="Use this server for semantic search of codebases")
+
+
+if __name__ == "__main__":
+    # Initialize and run the server
+    print("Starting demo mpc server...")
+    mcp.run(transport="stdio")
+
+```
+
+## Step 2: Create the search tool
+
+Let's implement the semantic search tool.
+
+server.py
+```python
+from codegen.extensions.tools.semantic_search import semantic_search
+
+....
+
+@mcp.tool('codebase_semantic_search', "search codebase with the provided query")
+def search(query: Annotated[str, "search query to run against codebase"]):
+  codebase = Codebase("provide location to codebase", programming_language="provide codebase Language")
+  # use the semantic search tool from codegen.extenstions.tools OR write your own   
+  results = semantic_search(codebase=codebase, query=query) 
+  return results
+
+....
+```
+
+## Run Your MCP Server
+
+You can run and inspect your MCP server with:
+
+```
+mcp dev server.py
+```
+
+If you'd like to integrate this into an IDE checkout out this [setup guide](/introduction/ide-usage#mcp-server-setup)
+
+And that's a wrap, chime in at our [community
+  Slack](https://community.codegen.com) if you have quesions or ideas for additional MCP tools/capabilities

pyproject.toml

@@ -68,6 +68,7 @@ dependencies = [
   "langchain_core",
   "langchain_openai",
   "numpy>=2.2.2",
+  "mcp[cli]",
 ]
 
 license = { text = "Apache-2.0" }

src/codegen/cli/mcp/agent/docs_expert.py

@@ -0,0 +1,63 @@
+"""Demo implementation of an agent with Codegen tools."""
+
+from langchain_core.messages import BaseMessage
+from langchain_core.runnables.history import RunnableWithMessageHistory
+
+from codegen.extensions.langchain.agent import create_codebase_agent
+from codegen.sdk.core.codebase import Codebase
+
+AGENT_INSTRUCTIONS = """
+Instruction Set for Codegen SDK Expert Agent
+
+Overview:
+This instruction set is designed for an agent that is an expert on the Codegen SDK, specifically the Python library. The agent will be asked questions about the SDK, including classes, utilities, properties, and how to accomplish tasks using the SDK. The goal is to provide helpful responses that assist users in achieving their tasks with the SDK.
+
+Key Responsibilities:
+1. Expertise in Codegen SDK:
+   - The agent is an expert on the Codegen SDK, with a deep understanding of its components and functionalities.
+   - It should be able to provide detailed explanations of classes, utilities, and properties defined in the SDK.
+
+2. Answering Questions:
+   - The agent will be asked questions about the Codegen SDK, such as:
+     - "Find all imports"
+     - "How do I add an import for a symbol?"
+     - "What is a statement object?"
+   - Responses should be clear, concise, and directly address the user's query.
+
+3. Task-Oriented Responses:
+   - The user is typically accomplishing a task using the Codegen SDK.
+   - Responses should be helpful toward that goal, providing guidance and solutions that facilitate task completion.
+
+4. Python Library Focus:
+   - Assume that questions are related to the Codegen SDK Python library.
+   - Provide Python-specific examples and explanations when applicable.
+
+Use the provided agent tools to look up additional information if needed.
+By following this instruction set, the agent will be well-equipped to assist users in effectively utilizing the Codegen SDK for their projects.
+"""
+
+
+def create_sdk_expert_agent(
+    codebase: Codebase,
+    model_name: str = "gpt-4o",
+    temperature: float = 0,
+    verbose: bool = True,
+) -> RunnableWithMessageHistory:
+    """Create an agent with all codebase tools.
+
+    Args:
+        codebase: The codebase to operate on
+        model_name: Name of the model to use (default: gpt-4)
+        temperature: Model temperature (default: 0)
+        verbose: Whether to print agent's thought process (default: True)
+
+    Returns:
+        Initialized agent with message history
+    """
+    # Initialize language model
+
+    system_message: BaseMessage = BaseMessage(content=AGENT_INSTRUCTIONS, type="SYSTEM")
+
+    agent = create_codebase_agent(chat_history=[system_message], codebase=codebase, model_name=model_name, temperature=temperature, verbose=verbose)
+
+    return agent

src/codegen/cli/mcp/server.py

@@ -3,8 +3,10 @@
 from mcp.server.fastmcp import Context, FastMCP
 
 from codegen.cli.api.client import RestAPI
+from codegen.cli.mcp.agent.docs_expert import create_sdk_expert_agent
 from codegen.cli.mcp.resources.system_prompt import SYSTEM_PROMPT
 from codegen.cli.mcp.resources.system_setup_instructions import SETUP_INSTRUCTIONS
+from codegen.sdk.core.codebase import Codebase
 from codegen.shared.enums.programming_language import ProgrammingLanguage
 
 # Initialize FastMCP server
@@ -39,6 +41,19 @@ def get_service_config() -> dict[str, Any]:
 # ----- TOOLS -----
 
 
+@mcp.tool()
+def ask_codegen_sdk(query: Annotated[str, "Ask a question to an exper agent for details about any aspect of the codegen sdk core set of classes and utilities"]):
+    codebase = Codebase("../../sdk/core")
+    agent = create_sdk_expert_agent(codebase=codebase)
+
+    result = agent.invoke(
+        {"input": query},
+        config={"configurable": {"session_id": "demo"}},
+    )
+
+    return result["output"]
+
+
 @mcp.tool()
 def generate_codemod(
     title: Annotated[str, "The title of the codemod (hyphenated)"],

src/codegen/extensions/langchain/__init__.py

@@ -1,9 +1,10 @@
 """Demo implementation of an agent with Codegen tools."""
 
-from langchain import hub
 from langchain.agents import AgentExecutor
 from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent
-from langchain_community.chat_message_histories import ChatMessageHistory
+from langchain.hub import pull
+from langchain_core.chat_history import InMemoryChatMessageHistory
+from langchain_core.messages import BaseMessage
 from langchain_core.runnables.history import RunnableWithMessageHistory
 from langchain_openai import ChatOpenAI
 
@@ -30,6 +31,7 @@ def create_codebase_agent(
     model_name: str = "gpt-4o",
     temperature: float = 0,
     verbose: bool = True,
+    chat_history: list[BaseMessage] = [],
 ) -> RunnableWithMessageHistory:
     """Create an agent with all codebase tools.
 
@@ -65,7 +67,7 @@ def create_codebase_agent(
     ]
 
     # Get the prompt to use
-    prompt = hub.pull("hwchase17/openai-functions-agent")
+    prompt = pull("hwchase17/openai-functions-agent")
 
     # Create the agent
     agent = OpenAIFunctionsAgent(
@@ -82,7 +84,69 @@ def create_codebase_agent(
     )
 
     # Create message history handler
-    message_history = ChatMessageHistory()
+    message_history = InMemoryChatMessageHistory(messages=chat_history)
+
+    # Wrap with message history
+    return RunnableWithMessageHistory(
+        agent_executor,
+        lambda session_id: message_history,
+        input_messages_key="input",
+        history_messages_key="chat_history",
+    )
+
+
+def create_codebase_inspector_agent(
+    codebase: Codebase,
+    model_name: str = "gpt-4o",
+    temperature: float = 0,
+    verbose: bool = True,
+    chat_history: list[BaseMessage] = [],
+) -> RunnableWithMessageHistory:
+    """Create an agent with all codebase tools.
+
+    Args:
+        codebase: The codebase to operate on
+        model_name: Name of the model to use (default: gpt-4)
+        temperature: Model temperature (default: 0)
+        verbose: Whether to print agent's thought process (default: True)
+
+    Returns:
+        Initialized agent with message history
+    """
+    # Initialize language model
+    llm = ChatOpenAI(
+        model_name=model_name,
+        temperature=temperature,
+    )
+
+    # Get all codebase tools
+    tools = [
+        ViewFileTool(codebase),
+        ListDirectoryTool(codebase),
+        SearchTool(codebase),
+        DeleteFileTool(codebase),
+        RevealSymbolTool(codebase),
+    ]
+
+    # Get the prompt to use
+    prompt = pull("codegen-agent/codebase-agent")
+
+    # Create the agent
+    agent = OpenAIFunctionsAgent(
+        llm=llm,
+        tools=tools,
+        prompt=prompt,
+    )
+
+    # Create the agent executor
+    agent_executor = AgentExecutor(
+        agent=agent,
+        tools=tools,
+        verbose=verbose,
+    )
+
+    # Create message history handler
+    message_history = InMemoryChatMessageHistory(messages=chat_history)
 
     # Wrap with message history
     return RunnableWithMessageHistory(