🚀 In this module, you’ll learn how to build a powerful GitHub search agent with optimized Agentic RAG capabilities and expose it as a Model Context Protocol (MCP) Server to supercharge your Cursor IDE experience. By building this agent, you’ll understand how to create practical AI tools that integrate seamlessly with your development workflow, improving coding efficiency and access to real-time code examples.

​

🔍 Creating Your GitHub Search Agent

​

Step 1: Set up xpander.ai

1. Navigate to the xpander.ai platform:

   Terminal Command

   # Open in your browser
   open https://app.xpander.ai
   

2. Sign in with your existing credentials or sign up for a new account

3. In the left navigation menu, go to Agents

4. Click the + New Agent button to open the Workbench

5. When prompted by the agent builder, click Skip (we’ll create the agent manually for this workshop)

​

Step 2: Add GitHub Search Tools

1. Click on the + (plus) button in your agent canvas

2. Select Apps from the menu

3. Select GitHub Search Manager

4. Click Sign in with GitHub Search Manager

5. Give the interface a name (like “github-search-your-name” or just “github-search”)

6. Click Save

​

Step 3: Add GitHub Operations

Add the following GitHub operations to your agent - select and confirm by clicking “Add to agent” at the bottom of the right selection menu.

• Find Code Snippets by Query Terms - GET /search/code
• Find Repositories by Criteria - GET /search/repositories
• Search Topics by Criteria - GET /search/topics
• Find Users by Criteria - GET /search/users
• Search Commits by Criteria - GET /search/commits

​

Step 4: Configure Agent Settings

1. In the AI Workbench, click on the Gear Icon in the top-right corner

2. In the Agent Builder Settings panel, click on Generate Details

3. Enter the following prompt to generate your agent description:

   Prompt for generating agent

   You are GitHub-Search-Agent, an expert at searching and analyzing GitHub repositories, code, users, and topics. You have deep knowledge of GitHub's API and can provide concise, accurate information about code patterns, repository structures, and developer activities.
   

👉 This step automatically generates the AI Agent instructions for us. Let’s further optimize them to match our exact use-case.

4. Go to the “Instructions” section and define your agent’s role, goal, and instructions:

   Role

   You are GitHub-Search-Agent, an expert at searching and analyzing GitHub repositories, code, users, and topics. You have deep knowledge of GitHub's API and can provide concise, accurate information about code patterns, repository structures, and developer activities.
   

   Goal

   Your goal is to help users find relevant code, repositories, and developers on GitHub to accelerate their development process. You should prioritize providing the most relevant results and explaining code patterns when found.
   

   Instructions

   1. When searching for code, focus on finding the most relevant implementations
   2. For repositories, highlight the most popular ones with good documentation
   3. When exploring user profiles, focus on their recent contributions and expertise
   4. Prefer repositories with higher star counts and recent activity
   5. For any search query, first try to understand what the user is looking for before running the search
   6. Always provide contextual information about why a result is relevant
   

5. Click Save Changes and close the settings window and then click Deploy.

Your agent should look similar to this:

​

🧪 Testing Your Agent with Raw API Responses

Let’s test your newly created GitHub search agent:

1. In the left pane of the builder interface, you’ll find the testing area

2. Type the following query to search for topics:

   Prompt

   Search for topics in GitHub related to A2A and MCP
   

3. Look at the generated payload by clicking on it. You’ll see something like:

   Example AI Generated Payload

   {
      "bodyParams": {},
      "queryParams": {
         "q": "A2A MCP",
         "per-page": 10,
         "page": 1
      },
      "pathParams": {}
   }
   

   Example response

   {
    "total_count": 2,
    "incomplete_results": false,
    "items": [
        {
            "name": "a2a-mcp",
            "created_at": "2025-04-30T00:52:27Z",
            "updated_at": "2025-04-30T00:52:27Z",
            "featured": false,
            "curated": false,
            "score": 1
        },
        {
            "name": "a2a-vs-mcp",
            "created_at": "2025-04-30T00:52:27Z",
            "updated_at": "2025-04-30T00:52:27Z",
            "featured": false,
            "curated": false,
            "score": 1
        }
    ]
   }
   

4. Now try a repositories search in the testing area:

   Prompt

   Find cool repositories related to Agent 2 Agent and MCP
   

​

🧠 Optimizing Responses with Agentic RAG

​

Optimizing Repository Search

Let’s improve the repository search results using Agentic RAG:

1. You can configure specific tool calls. Click on the gear icon next to the Find Repositories by Criteria operation in the graph.

2. Click on Advanced and then switch to Raw Editor (this allows the AI to send natural language queries, and the server will perform semantic search on the API response)

3. Configure the search and return fields:

   Searchable Fields (fields the AI can search against):

   Searchable Fields Configuration

   [
     "items[].full_name",
     "items[].description",
     "items[].topics"
   ]
   

   Returnable Fields (data the AI will receive):

   Returnable Fields Configuration

   [
     "items[].html_url",
     "items[].full_name",
     "items[].description"
   ]
   

4. Save and deploy your changes

5. Now run the repositories query again:

   Prompt

   Find cool repos related to A2A and/or MCP
   

​

Configure Agentic RAG for the other operations as well

Go through the three tabs in this section to configure the correct Searchable and Returnable fields for the agent’s tools, in order to use Agentic RAG to optimize the tool calls.

[
  "items[].login"
]

[
  "items[].html_url",
  "items[].login", 
  "items[].avatar_url"
]

[
  "items[].login"
]

[
  "items[].html_url",
  "items[].login", 
  "items[].avatar_url"
]

[
  "items[].commit.message",
  "items[].author.login",
  "items[].commit.author.name",
  "items[].repository.full_name"
]

[
  "items[].html_url",
  "items[].sha",
  "items[].commit.message",
  "items[].commit.author.date",
  "items[].author.login", 
  "items[].repository.html_url"
]

[
  "items[].name",
  "items[].path",
  "items[].repository.full_name",
  "items[].repository.description"
]

[
  "items[].html_url",
  "items[].path",
  "items[].repository.html_url",
  "items[].repository.full_name"
]

​

Continue from the workshop from the WebUI

To continue testing your agent:

1. Keep the AI Workbench tab open
2. Open the web chat interface by clicking the Chat source node (at the top of the canvas) and clicking the URL.

3. Select the last conversation thread you started before configuring the agentic RAG from your chat history to maintain context

Now test your agent and check the generated payload for each and the response of the Agent. Here are few Prompt optimized to ask the Agent to select specific operation. You want to see if the data returned is optimized or not. You can use the same thread or create new one.

Find a few GitHub users related to MCP

Summarize the recent commits of users related to MCP repositories

Find the implementation of A2A and return the code snippet

​

📄 Adding Code Reading Capabilities

Let’s enhance our agent by adding the ability to read code directly from GitHub URLs:

1. In the left navigation menu, go to Cloud Functions
2. Click the New button
3. Replace the example code with the following snippet:

# === GitHub Code Reader ===
# Contract-name: xpander_run_action
# Purpose: Retrieve any file's raw text from GitHub (public or private) so downstream
#          agents can parse the code.

# import requests – auto-imported by the runtime
import os

def xpander_run_action(repo_full_name: str, file_path: str, ref: str = "main"):
    # GitHub Code Reader
    # ------------------
    # Args:
    #     repo_full_name : str  | "owner/repo", e.g. "org/project"
    #     file_path      : str  | path within the repo, e.g. "src/app.py"
    #     ref            : str  | branch, tag, or commit SHA (default "main")
    # Optional ENV:
    #     GIT_TOKEN            | Personal Access Token for private repositories.
    #                           If present, it's sent as a Bearer token.
    # Returns:
    #     str – the file's text on success, or an error string on failure.

    raw_url = f"https://raw.githubusercontent.com/{repo_full_name}/{ref}/{file_path}"

    headers = {}
    token = os.getenv("GIT_TOKEN")
    if token:
        headers["Authorization"] = f"Bearer {token}"

    try:
        resp = requests.get(raw_url, headers=headers, timeout=15)
        resp.raise_for_status()
    except requests.RequestException as exc:
        return f"Request failed: {exc}"

    return resp.text

4. Name it GitHub Code Reader and click Save
5. Return to your agent in the Agents section
6. Click the + button
7. Select Custom Action
8. Add the GitHub Code Reader function to your agent canvas. It could take a minute for the Action to appear in the menu as it gets validated. Refresh the Agent page if you don’t see it.
9. Click Deploy

Run the following prompt

How can I increase throughput using cross-region inference with Amazon Bedrock and boto3? Download and provide the real python code snippet from AWS repos, not a link

​

🌎 Enhancing Your Agent with Web Search

Your GitHub search agent can currently only access information available on GitHub. Let’s add web search capabilities to make it more versatile:

1. Return to your AI Agent workbench

2. Click the + button to add components to the AI Agent

3. Select Built-in Actions

4. Add Fetch Tavily AI Insights (for comprehensive web searching)

5. Click on the gear icon in your agent canvas to edit the agent settings

6. Update your agent’s instructions by adding these important rules:

   Updated Instructions

   7. ALWAYS start with Tavily AI Insights to gather general information, but NEVER stop there
   8. After using Tavily, ALWAYS follow up with relevant GitHub searches to find concrete code examples and repositories
   9. For every user query, you must provide both web-based context AND GitHub-specific examples
   

7. Save & Deploy your changes

8. In the Tester tab, try a query that requires recent information:

   Test Prompt

   What are the latest changes to the A2A protocol specification? Show me example implementations.
   

Your agent should now provide more comprehensive answers by combining web search results with GitHub code examples:

​

🔄 Creating the Agent Dependency Graph

In production, to ensure your agent follows the right search strategy, we should always create a dependency graph that deterministically enforces specific business logic behavior:

1. In your agent canvas, by attaching lines between operations, you can connect your nodes to create the dependency graph
2. Set up a workflow that requires an internet search before accessing GitHub operations:
   • Connect the Tavily AI Insights node as a prerequisite to the GitHub operations - simply drag lines from the top of all the GitHub operations to the bottom of the Tavily operation.
   • Ensure the flow follows a logical progression

3. Repeat the query with explict request to not start with Tavily

What are the latest changes to the A2A protocol specification? Show me example implementations. Use GitHub without Tavily

4. Now run a prompt that does not conflict with the dependency graph:

Using only Google's A2A protocol implementation, create an agent that responds with 'hello world'. Provide the Python code.

5. Verify that the AI follows the correct workflow - first searching for information before accessing GitHub operations:

​

🌐 Exposing Your Agent as an MCP server

​

Step 1: Access your Agent MCP URL

The final step is to expose your optimized GitHub agent as a Model Context Protocol (MCP) server:

1. In your agent canvas, click the + button
2. Select Sources → MCP
3. Click Deploy

4. After the short deployment phase completes, you’ll receive a unique URL for your MCP server when you click on the MCP source node on the canvas.
5. Configure this endpoint in your Cursor IDE

Go to Cursor -> Settings -> MCP and click on ”+ add new global MCP Server” or edit directly the ~/.cursor/mcp.json:

{
  "mcpServers":{
    "xpander-github-mcp": {
      "command": "npx",
      "args": [
        "xpander-mcp-remote",
        "https://mcp.xpander.ai/your-server-url/"
      ]
    }
  }
}

​

Step 2: Customize the Cursor Settings

1. Add an explicit instruction to prioritize MCP commands in the “system prompt” configuration of your Cursor IDE.

If relevant to the question, always use MCP tools when available before answering

2. Make sure the MCP server is active (Cursor Settings -> MCP)

3. Restart Cursor IDE to apply the changes

​

Step 3: Ask Cursor Agent to invoke MCP

Test your MCP integration by providing this prompt in the Cursor AI Agent section:

search for code samples of xpander-ai sdk

​

✅ Checkpoint

Congratulations! By completing this module, you should now be able to:

1. Create and configure a GitHub search agent with optimized Agentic RAG
2. Customize searchable and returnable fields for each tool call
3. Add custom code execution to your agent
4. Structure agent workflows using dependency graphs
5. Deploy your agent as an MCP server for Cursor IDE

​

🔄 Next Steps

Now that you’ve supercharged your IDE with an optimized GitHub MCP server, you’re ready to build your first coding agent - proceed to the next module.