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Hugging Face MCP Server

by NimbleBrainInc
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MIT License
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Hugging Face MCP Server

MCP server for accessing the Hugging Face Inference API. Run 200,000+ machine learning models including LLMs, image generation, text classification, embeddings, and more.

Features

  • Text Generation: LLMs like Llama-3, Mistral, Gemma

  • Image Generation: FLUX, Stable Diffusion XL, SD 2.1

  • Text Classification: Sentiment analysis, topic classification

  • Token Classification: Named entity recognition, POS tagging

  • Question Answering: Extract answers from context

  • Summarization: Condense long text

  • Translation: 200+ language pairs

  • Image-to-Text: Image captioning

  • Image Classification: Classify images into categories

  • Object Detection: Detect objects with bounding boxes

  • Text-to-Speech: Convert text to audio

  • Speech Recognition: Transcribe audio (Whisper)

  • Embeddings: Get text/sentence embeddings

  • And more: Fill-mask, sentence similarity

Setup

Prerequisites

  • Hugging Face account

  • API token (free or Pro)

Environment Variables

  • HUGGINGFACE_API_TOKEN (required): Your Hugging Face API token

How to get an API token:

  1. Go to huggingface.co/settings/tokens

  2. Click "New token"

  3. Give it a name and select permissions (read is sufficient for inference)

  4. Copy the token (starts with hf_)

  5. Store as HUGGINGFACE_API_TOKEN

Available Tools

Text Generation Tools

text_generation

Generate text using large language models.

Parameters:

  • prompt (string, required): Input text prompt

  • model_id (string, optional): Model ID (default: 'mistralai/Mistral-7B-Instruct-v0.3')

  • max_new_tokens (int, optional): Maximum tokens to generate

  • temperature (float, optional): Sampling temperature 0-2 (higher = more random)

  • top_p (float, optional): Nucleus sampling 0-1

  • top_k (int, optional): Top-k sampling

  • repetition_penalty (float, optional): Penalty for repetition

  • return_full_text (bool, optional): Return prompt + generation (default: False)

Popular models:

  • meta-llama/Llama-3.2-3B-Instruct - Meta's Llama 3.2

  • mistralai/Mistral-7B-Instruct-v0.3 - Mistral 7B

  • google/gemma-2-2b-it - Google Gemma 2

  • HuggingFaceH4/zephyr-7b-beta - Zephyr 7B

  • tiiuae/falcon-7b-instruct - Falcon 7B

Example:

result = await text_generation( prompt="Write a Python function to calculate fibonacci numbers:", model_id="mistralai/Mistral-7B-Instruct-v0.3", max_new_tokens=200, temperature=0.7, top_p=0.9 )

Classification Tools

text_classification

Classify text into categories (sentiment, topics, etc.).

Parameters:

  • text (string, required): Text to classify

  • model_id (string, optional): Model ID (default: 'distilbert-base-uncased-finetuned-sst-2-english')

Popular models:

  • distilbert-base-uncased-finetuned-sst-2-english - Sentiment (positive/negative)

  • facebook/bart-large-mnli - Zero-shot classification

  • cardiffnlp/twitter-roberta-base-sentiment-latest - Twitter sentiment

  • finiteautomata/bertweet-base-sentiment-analysis - Tweet sentiment

Example:

result = await text_classification( text="I love this product! It exceeded my expectations.", model_id="distilbert-base-uncased-finetuned-sst-2-english" ) # Returns: [{'label': 'POSITIVE', 'score': 0.9998}]

token_classification

Token-level classification for NER, POS tagging, etc.

Parameters:

  • text (string, required): Input text

  • model_id (string, optional): Model ID (default: 'dslim/bert-base-NER')

Popular models:

  • dslim/bert-base-NER - Named Entity Recognition

  • Jean-Baptiste/roberta-large-ner-english - Large NER model

  • dbmdz/bert-large-cased-finetuned-conll03-english - CoNLL-2003 NER

Example:

result = await token_classification( text="Apple Inc. is located in Cupertino, California.", model_id="dslim/bert-base-NER" ) # Returns entities: ORG (Apple Inc.), LOC (Cupertino), LOC (California)

Question Answering & Text Processing

question_answering

Answer questions based on provided context.

Parameters:

  • question (string, required): Question to answer

  • context (string, required): Context containing the answer

  • model_id (string, optional): Model ID (default: 'deepset/roberta-base-squad2')

Popular models:

  • deepset/roberta-base-squad2 - RoBERTa on SQuAD 2.0

  • distilbert-base-cased-distilled-squad - DistilBERT on SQuAD

Example:

result = await question_answering( question="Where is the Eiffel Tower located?", context="The Eiffel Tower is a landmark in Paris, France. It was built in 1889.", model_id="deepset/roberta-base-squad2" ) # Returns: {'answer': 'Paris, France', 'score': 0.98, 'start': 35, 'end': 48}

summarization

Summarize long text into shorter version.

Parameters:

  • text (string, required): Text to summarize

  • model_id (string, optional): Model ID (default: 'facebook/bart-large-cnn')

  • max_length (int, optional): Maximum summary length

  • min_length (int, optional): Minimum summary length

Popular models:

  • facebook/bart-large-cnn - BART CNN summarization

  • google/pegasus-xsum - PEGASUS XSum

  • sshleifer/distilbart-cnn-12-6 - Distilled BART

Example:

result = await summarization( text="Long article text here...", model_id="facebook/bart-large-cnn", max_length=130, min_length=30 )

translation

Translate text between languages.

Parameters:

  • text (string, required): Text to translate

  • model_id (string, required): Model ID for language pair

Popular models:

  • Helsinki-NLP/opus-mt-en-es - English to Spanish

  • Helsinki-NLP/opus-mt-es-en - Spanish to English

  • Helsinki-NLP/opus-mt-en-fr - English to French

  • Helsinki-NLP/opus-mt-en-de - English to German

  • facebook/mbart-large-50-many-to-many-mmt - Multilingual (50 languages)

Example:

result = await translation( text="Hello, how are you?", model_id="Helsinki-NLP/opus-mt-en-es" ) # Returns: "Hola, ¿cómo estás?"

Image Generation Tools

text_to_image

Generate images from text prompts.

Parameters:

  • prompt (string, required): Text description of desired image

  • model_id (string, optional): Model ID (default: 'black-forest-labs/FLUX.1-dev')

  • negative_prompt (string, optional): What to avoid in image

  • num_inference_steps (int, optional): Number of denoising steps

  • guidance_scale (float, optional): How closely to follow prompt

Popular models:

  • black-forest-labs/FLUX.1-dev - FLUX.1 (high quality)

  • stabilityai/stable-diffusion-xl-base-1.0 - SDXL

  • stabilityai/stable-diffusion-2-1 - SD 2.1

  • runwayml/stable-diffusion-v1-5 - SD 1.5

Example:

result = await text_to_image( prompt="A serene mountain landscape at sunset, photorealistic, 8k", model_id="black-forest-labs/FLUX.1-dev", negative_prompt="blurry, low quality, distorted", guidance_scale=7.5 ) # Returns: {'image': 'base64_encoded_image', 'format': 'base64'}

Computer Vision Tools

image_to_text

Generate text descriptions from images (captioning).

Parameters:

  • image_base64 (string, required): Base64 encoded image

  • model_id (string, optional): Model ID (default: 'Salesforce/blip-image-captioning-large')

Popular models:

  • Salesforce/blip-image-captioning-large - BLIP large

  • nlpconnect/vit-gpt2-image-captioning - ViT-GPT2

Example:

result = await image_to_text( image_base64="base64_encoded_image_data", model_id="Salesforce/blip-image-captioning-large" ) # Returns: [{'generated_text': 'a dog playing in the park'}]

image_classification

Classify images into categories.

Parameters:

  • image_base64 (string, required): Base64 encoded image

  • model_id (string, optional): Model ID (default: 'google/vit-base-patch16-224')

Popular models:

  • google/vit-base-patch16-224 - Vision Transformer

  • microsoft/resnet-50 - ResNet-50

Example:

result = await image_classification( image_base64="base64_encoded_image_data", model_id="google/vit-base-patch16-224" ) # Returns: [{'label': 'golden retriever', 'score': 0.95}, ...]

object_detection

Detect objects in images with bounding boxes.

Parameters:

  • image_base64 (string, required): Base64 encoded image

  • model_id (string, optional): Model ID (default: 'facebook/detr-resnet-50')

Popular models:

  • facebook/detr-resnet-50 - DETR with ResNet-50

  • hustvl/yolos-tiny - YOLOS tiny

Example:

result = await object_detection( image_base64="base64_encoded_image_data", model_id="facebook/detr-resnet-50" ) # Returns: [{'label': 'dog', 'score': 0.98, 'box': {...}}, ...]

Audio Tools

text_to_speech

Convert text to speech audio.

Parameters:

  • text (string, required): Text to synthesize

  • model_id (string, optional): Model ID (default: 'facebook/mms-tts-eng')

Popular models:

  • facebook/mms-tts-eng - MMS TTS English

  • espnet/kan-bayashi_ljspeech_vits - VITS LJSpeech

Example:

result = await text_to_speech( text="Hello, this is a test of text to speech.", model_id="facebook/mms-tts-eng" ) # Returns: {'audio': 'base64_encoded_audio', 'format': 'base64'}

automatic_speech_recognition

Transcribe audio to text (speech recognition).

Parameters:

  • audio_base64 (string, required): Base64 encoded audio

  • model_id (string, optional): Model ID (default: 'openai/whisper-large-v3')

Popular models:

  • openai/whisper-large-v3 - Whisper large v3 (best quality)

  • openai/whisper-medium - Whisper medium (faster)

  • facebook/wav2vec2-base-960h - Wav2Vec 2.0

Example:

result = await automatic_speech_recognition( audio_base64="base64_encoded_audio_data", model_id="openai/whisper-large-v3" ) # Returns: {'text': 'transcribed audio text here'}

Embedding & Similarity Tools

sentence_similarity

Compute similarity between sentences.

Parameters:

  • source_sentence (string, required): Reference sentence

  • sentences (list, required): List of sentences to compare

  • model_id (string, optional): Model ID (default: 'sentence-transformers/all-MiniLM-L6-v2')

Popular models:

  • sentence-transformers/all-MiniLM-L6-v2 - Fast, good quality

  • sentence-transformers/all-mpnet-base-v2 - Best quality

  • BAAI/bge-small-en-v1.5 - BGE small

Example:

result = await sentence_similarity( source_sentence="The cat sits on the mat", sentences=[ "A cat is sitting on a mat", "The dog runs in the park", "Cats are great pets" ], model_id="sentence-transformers/all-MiniLM-L6-v2" ) # Returns: [0.95, 0.23, 0.65]

feature_extraction

Get embeddings (feature vectors) for text.

Parameters:

  • text (string, required): Input text

  • model_id (string, optional): Model ID (default: 'sentence-transformers/all-MiniLM-L6-v2')

Popular models:

  • sentence-transformers/all-MiniLM-L6-v2 - 384 dimensions

  • sentence-transformers/all-mpnet-base-v2 - 768 dimensions

  • BAAI/bge-small-en-v1.5 - 384 dimensions

Example:

result = await feature_extraction( text="This is a sample sentence.", model_id="sentence-transformers/all-MiniLM-L6-v2" ) # Returns: [[0.012, -0.034, 0.056, ...]] (384-dimensional vector)

fill_mask

Fill in masked words in text.

Parameters:

  • text (string, required): Text with [MASK] token

  • model_id (string, optional): Model ID (default: 'bert-base-uncased')

Popular models:

  • bert-base-uncased - BERT base

  • roberta-base - RoBERTa base

  • distilbert-base-uncased - DistilBERT

Example:

result = await fill_mask( text="Paris is the [MASK] of France.", model_id="bert-base-uncased" ) # Returns: [{'token_str': 'capital', 'score': 0.95}, ...]

Model Loading & Cold Starts

Important: Models may take 20-60 seconds to load on first request (cold start). Subsequent requests are faster.

Tips:

  • Use popular models for faster loading

  • Implement retry logic for timeouts

  • Consider caching model responses

  • Use smaller models for faster inference

Rate Limits

Free Tier

  • Rate limited to prevent abuse

  • Suitable for testing and small projects

  • May experience queuing during high load

Pro Subscription ($9/month)

  • No rate limits

  • Priority access to models

  • Faster inference

  • No queuing

Visit huggingface.co/pricing for details.

Base64 Encoding

For images and audio, you need to provide base64 encoded data:

Python example:

import base64 # Encode image with open("image.jpg", "rb") as f: image_base64 = base64.b64encode(f.read()).decode('utf-8') # Encode audio with open("audio.wav", "rb") as f: audio_base64 = base64.b64encode(f.read()).decode('utf-8') # Decode image response image_bytes = base64.b64decode(response['image']) with open("generated.jpg", "wb") as f: f.write(image_bytes)

Parameter Tuning

Text Generation

  • temperature (0-2): Higher = more creative/random, Lower = more focused/deterministic

  • top_p (0-1): Nucleus sampling, typically 0.9-0.95

  • top_k: Number of highest probability tokens to keep

  • repetition_penalty: Penalize repeated tokens (>1.0 reduces repetition)

Image Generation

  • guidance_scale (1-20): Higher = follows prompt more strictly (typical: 7-7.5)

  • num_inference_steps: More steps = higher quality but slower (typical: 20-50)

  • negative_prompt: Describe what you don't want in the image

Error Handling

Common errors:

  • 503 Service Unavailable: Model is loading (cold start), retry after 20-60 seconds

  • 401 Unauthorized: Invalid or missing API token

  • 429 Too Many Requests: Rate limit exceeded (upgrade to Pro)

  • 400 Bad Request: Invalid parameters or model ID

  • 504 Gateway Timeout: Model took too long to respond

Retry logic example:

import time max_retries = 3 for attempt in range(max_retries): try: result = await text_generation(prompt="Hello") break except httpx.HTTPStatusError as e: if e.response.status_code == 503 and attempt < max_retries - 1: time.sleep(20) # Wait for model to load continue raise

Finding Models

Browse models:

  • Visit huggingface.co/models

  • Filter by task (Text Generation, Image Generation, etc.)

  • Sort by downloads, likes, or trending

  • Check model card for usage examples

Popular categories:

  • Text Generation: 50,000+ models

  • Text Classification: 30,000+ models

  • Image Generation: 10,000+ models

  • Translation: 5,000+ models

  • Embeddings: 3,000+ models

Best Practices

  1. Use popular models: Faster loading and better maintained

  2. Implement timeouts: Set appropriate timeouts (60-120 seconds)

  3. Cache responses: Store results to reduce API calls

  4. Handle cold starts: Implement retry logic for 503 errors

  5. Monitor usage: Track API calls and costs

  6. Test locally: Use Hugging Face Transformers library for testing

  7. Read model cards: Understand model capabilities and limitations

  8. Optimize parameters: Tune settings for your use case

Use Cases

  • Chatbots: LLM-powered conversational AI

  • Content Generation: Blog posts, articles, creative writing

  • Image Creation: Art, illustrations, product images

  • Sentiment Analysis: Customer feedback analysis

  • Translation: Multi-language support

  • Transcription: Meeting notes, podcast transcripts

  • Semantic Search: Embedding-based search

  • Data Extraction: NER for document processing

  • Content Moderation: Text and image classification

API Documentation

Support

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How are these scores calculated?

remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

Enables access to 200,000+ machine learning models through the Hugging Face Inference API. Supports text generation, image creation, classification, translation, speech processing, embeddings, and more AI tasks.

  1. Features
    1. Setup
      1. Prerequisites
      2. Environment Variables
    2. Available Tools
      1. Text Generation Tools
      2. Classification Tools
      3. Question Answering & Text Processing
      4. Image Generation Tools
      5. Computer Vision Tools
      6. Audio Tools
      7. Embedding & Similarity Tools
    3. Model Loading & Cold Starts
      1. Rate Limits
        1. Free Tier
        2. Pro Subscription ($9/month)
      2. Base64 Encoding
        1. Parameter Tuning
          1. Text Generation
          2. Image Generation
        2. Error Handling
          1. Finding Models
            1. Best Practices
              1. Use Cases
                1. API Documentation
                  1. Support

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