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import json
from typing import Literal
from datetime import datetime
import gradio as gr
from huggingface_hub import list_models, model_info, hf_hub_download
def search_models(
search: str = None,
library: str = None,
tags: str = None,
pipeline_tag: str = None,
sort: Literal[
"trending_score", "last_modified", "created_at", "downloads", "likes"
] = "trending_score",
direction: Literal["descending", "ascending"] = "descending",
limit: int = 20,
) -> str:
"""
Search models on Hugging Face Hub.
Use this tool to search for models by name, tags, or other filters, and to get a list of model IDs.
This is the first step when you need to find a specific model before retrieving its details.
Parameters:
search (str, optional): A string to search for in model IDs or names (e.g., "deepseek").
library (list[str], optional): List of libraries the models use (e.g., ["pytorch", "tensorflow"]).
tags (list[str], optional): List of tags to filter models by (e.g., ["text-generation", "llama"]).
pipeline_tag (str, optional): Filter by pipeline tag (e.g., "text-generation").
sort (Literal["trending_score", "last_modified", "created_at", "downloads", "likes"], default="trending_score"): Sort models by the specified key.
direction (int, default=-1): Sort direction: -1 for descending, 1 for ascending.
limit (int, default=20): Maximum number of models to return.
Returns:
list[str]: A list of model IDs matching the search criteria.
Examples:
- To find trending models: search_models(sort="trending_score", limit=10)
- To search for models related to "deepseek": search_models(search="deepseek", sort="likes", limit=5)
- To filter by tag: search_models(tags=["text-generation"], pipeline_tag="text-generation")
"""
try:
library = library.split(",") if library else None
tags = tags.split(",") if tags else None
direction = -1 if direction == "descending" else 1
models = list_models(
library=library,
tags=tags,
search=search,
pipeline_tag=pipeline_tag,
sort=sort,
direction=direction,
limit=limit,
)
return json.dumps([model.modelId for model in models])
except Exception as e:
return f"Error: {e}"
def get_model_info(model_id: str) -> dict:
"""
Get structured metadata about a model on the Hugging Face Hub.
Use this when you need specific fields like downloads, tags, or other metadata.
For comprehensive model information, use `get_model_card`.
This tool requires the exact model ID, which can be obtained using `search_models`.
If you have a partial name or tag, use `search_models` first to find the exact ID.
Parameters:
model_id (str): The exact model ID in the format "organization/model-name" (e.g., "DeepSeek/DeepSeek-R1").
Returns:
dict: A dictionary containing model information including available fields such as:
- id: The model ID
- author: The author of the model
- created_at: The creation date
- last_modified: The last modified date
- downloads: Number of downloads
- likes: Number of likes
- tags: List of tags
- pipeline_tag: The pipeline tag
- library_name: The library name
- license: The model license
- base_model: The base model (if available)
- siblings: List of repository files (if available)
- datasets: Datasets used to train the model (if available)
- spaces: List of spaces using this model (if available)
- xet_enabled: Whether XET is enabled (if available)
Raises:
Exception: If the model_id is invalid or not found. Use search_models to find the correct ID.
Example:
- First, find the model ID: search_models(search="deepseek", sort="likes", limit=1)
- Then, get the model info: get_model_info("DeepSeek/DeepSeek-R1")
"""
try:
model = model_info(model_id)
result = {}
if hasattr(model, "id") and model.id is not None:
result["id"] = model.id
if hasattr(model, "author") and model.author is not None:
result["author"] = model.author
if hasattr(model, "created_at") and model.created_at is not None:
result["created_at"] = str(model.created_at)
if hasattr(model, "last_modified") and model.last_modified is not None:
result["last_modified"] = str(model.last_modified)
if hasattr(model, "downloads") and model.downloads is not None:
result["downloads"] = model.downloads
if hasattr(model, "likes") and model.likes is not None:
result["likes"] = model.likes
if hasattr(model, "tags") and model.tags is not None:
result["tags"] = model.tags
if hasattr(model, "pipeline_tag") and model.pipeline_tag is not None:
result["pipeline_tag"] = model.pipeline_tag
if hasattr(model, "library_name") and model.library_name is not None:
result["library_name"] = model.library_name
if hasattr(model, "card_data") and model.card_data is not None:
if (
hasattr(model.card_data, "license")
and model.card_data.license is not None
):
result["license"] = model.card_data.license
if (
hasattr(model.card_data, "base_model")
and model.card_data.base_model is not None
):
result["base_model"] = model.card_data.base_model
if (
hasattr(model.card_data, "datasets")
and model.card_data.datasets is not None
):
result["datasets"] = model.card_data.datasets
if hasattr(model, "siblings") and model.siblings is not None:
result["siblings"] = []
for s in model.siblings:
if isinstance(s, str):
result["siblings"].append(s)
else:
result["siblings"].append({
k: str(v) if isinstance(v, datetime) else v
for k, v in s.__dict__.items() if not k.startswith('_')
})
if hasattr(model, "spaces") and model.spaces is not None:
result["spaces"] = []
for s in model.spaces:
if isinstance(s, str):
result["spaces"].append(s)
else:
result["spaces"].append({
k: str(v) if isinstance(v, datetime) else v
for k, v in s.__dict__.items() if not k.startswith('_')
})
if hasattr(model, "xet_enabled") and model.xet_enabled is not None:
result["xet_enabled"] = model.xet_enabled
return json.dumps(result)
except Exception as e:
return f"Error: {e}"
def get_model_card(model_id: str) -> str:
"""
Get the complete model card (README.md) for a specific model on Hugging Face Hub.
Use this when you need comprehensive model documentation including usage examples, model limitations, etc.
For only structured metadata, use `get_model_info` instead.
This tool requires the exact model ID, which can be obtained using `search_models`.
If you have a partial name or tag, use `search_models` first to find the exact ID.
Args:
model_id (str): The model ID in the format "organization/model-name" (e.g., "DeepSeek/DeepSeek-R1").
Returns:
str: The markdown content of the model card.
Example:
- First, find the model ID: search_models(search="deepseek", sort="likes", limit=1)
- Then, get the model card: get_model_card("DeepSeek/DeepSeek-R1")
"""
try:
filepath = hf_hub_download(model_id, "README.md")
with open(filepath, "r", encoding="utf-8") as f:
content = f.read()
return content
except Exception as e:
return f"Error: {e}"
description_html = """
<h1>🤗 Hugging Face MCP Server</h1>
<p>Use AI Agents to interact with the Hugging Face Hub.</p>
<h3>Available tools:</h3>
<ul>
<li><strong>search_models</strong>: Find models by name, tags, etc.</li>
<li><strong>get_model_info</strong>: Get model metadata</li>
<li><strong>get_model_card</strong>: View model documentation</li>
</ul>
<h3>Setup (for Cursor):</h3>
<ol>
<li>Click <strong>"Use via API"</strong> → <strong>"MCP"</strong> tab</li>
<li>Copy SSE config:
<pre>
{
"mcpServers": {
"gradio": {
"url": "https://dylanebert-huggingface-mcp.hf.space/gradio_api/mcp/sse"
}
}
}
</pre>
</li>
<li>Paste in: <strong>Cursor Settings</strong> → <strong>MCP</strong> → <strong>Add Global MCP Server</strong></li>
<li>Chat in agent mode with prompts like: "list top 10 trending models on Hugging Face"</li>
</ol>
<hr>
"""
search_models = gr.Interface(
fn=search_models,
description=description_html,
inputs=[
gr.Textbox(label="search", value=""),
gr.Textbox(label="library", value=""),
gr.Textbox(label="tags", value=""),
gr.Textbox(label="pipeline_tag", value=""),
gr.Radio(label="sort", choices=["trending_score", "last_modified", "created_at", "downloads", "likes"], value="trending_score"),
gr.Radio(label="direction", choices=["descending", "ascending"], value="descending"),
gr.Number(label="limit", value=20),
],
outputs="text")
get_model_info = gr.Interface(
fn=get_model_info,
description=description_html,
inputs=[
gr.Textbox(label="model_id", value=""),
],
outputs="text")
get_model_card = gr.Interface(
fn=get_model_card,
description=description_html,
inputs=[
gr.Textbox(label="model_id", value=""),
],
outputs="text")
demo = gr.TabbedInterface(
interface_list=[search_models, get_model_info, get_model_card],
tab_names=["search_models", "get_model_info", "get_model_card"]
)
demo.launch(mcp_server=True)
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