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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 - https://www.gradio.app/guides/building-an-mcp-client-with-gradio
- Gradio MCP Integration - Hugging Face MCP Course - https://huggingface.co/learn/mcp-course/en/unit1/gradio-mcp
Use this space as MCP and create you own extensions |