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mcp_endpoint_guide.md

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+# 📖 The Quest Begins: Unveiling the MCP Endpoint
+
+Your expedition commenced with a simple GET request to the enigmatic URL:
+
+```
+https://huggingface.co/mcp
+```
+
+The server's response was a cryptic message:
+
+```json
+{ 
+  "jsonrpc": "2.0", 
+  "error": { 
+    "code": -32000, 
+    "message": "Method not allowed. Use POST for stateless JSON-RPC requests." 
+  }, 
+  "id": null 
+}
+```
+
+This indicated that the server adheres to the JSON-RPC 2.0 protocol and expects POST requests for interaction.
+
+## 🧠 Deciphering the Protocol: JSON-RPC 2.0 and MCP
+
+The Model Context Protocol (MCP) standardizes interactions between clients and servers, facilitating seamless communication with AI models and tools. It employs JSON-RPC 2.0 for message formatting and supports transport mechanisms like HTTP with Server-Sent Events (SSE) and stdio for local communication.
+
+## 🔍 Discovering Available Tools
+
+Adjusting your approach, you sent a POST request with the appropriate headers and payload:
+
+```json
+{ 
+  "jsonrpc": "2.0", 
+  "method": "tools/list", 
+  "params": {}, 
+  "id": 1 
+}
+```
+
+The server responded with a list of available tools, each accompanied by detailed descriptions and input schemas:
+
+- **space_search**: Search for Hugging Face Spaces.
+- **model_detail**: Retrieve detailed information about a specific model.
+- **paper_search**: Search for machine learning research papers.
+- **dataset_search**: Find datasets on Hugging Face.
+- **dataset_detail**: Get detailed information about a specific dataset.
+- **duplicate_space**: Duplicate a Hugging Face Space.
+
+## 🛠️ Crafting the Gradio Client
+
+To interact with these tools in a user-friendly manner, you can construct a Gradio interface. Here's a simplified example focusing on the `space_search` tool:
+
+```python
+import gradio as gr
+import requests
+import json
+
+MCP_URL = "https://huggingface.co/mcp"
+HEADERS = {
+    "Content-Type": "application/json",
+    "Accept": "application/json, text/event-stream"
+}
+
+def call_mcp_tool(method, params):
+    payload = {
+        "jsonrpc": "2.0",
+        "method": method,
+        "params": params,
+        "id": 1
+    }
+    response = requests.post(MCP_URL, headers=HEADERS, data=json.dumps(payload))
+    try:
+        return response.json()
+    except Exception as e:
+        return {"error": str(e), "raw_response": response.text}
+
+def space_search(query, limit=10, mcp=False):
+    params = {
+        "query": query,
+        "limit": limit,
+        "mcp": mcp
+    }
+    result = call_mcp_tool("tools/call", {"tool": "space_search", "input": params})
+    return result
+
+with gr.Blocks() as demo:
+    gr.Markdown("# MCP Gradio Client - Space Search")
+    
+    query = gr.Textbox(label="Query")
+    limit = gr.Slider(1, 50, value=10, label="Limit")
+    mcp = gr.Checkbox(label="MCP Only")
+    output = gr.JSON()
+    
+    btn = gr.Button("Search")
+    btn.click(space_search, inputs=[query, limit, mcp], outputs=output)
+
+demo.launch()
+```
+
+This interface allows users to input search queries and view results directly from the MCP server.
+
+## 🔗 Further Exploration
+
+For a comprehensive understanding and additional examples, consider exploring the following resources:
+
+- Building an MCP Client with Gradio
+- Gradio MCP Integration - Hugging Face MCP Course