Discover Awesome MCP Servers

Selector Mcp Server

Um servidor de Protocolo de Contexto de Modelo (MCP) que permite bate-papo de IA interativo e em tempo real com o Selector AI através de um servidor com capacidade de streaming e um cliente baseado em Docker que se comunica via stdin/stdout.

Model Context Protocol (MCP)

The Model Context Protocol is an open standard that enables developers to build secure, two-way connections between their data sources and AI-powered tools. The architecture is straightforward: developers can either expose their data through MCP servers or build AI applications (MCP clients) that connect to these servers.

MCP GO Tools

A Go-focused Model Context Protocol (MCP) server that provides idiomatic Go code generation, style guidelines, and best practices. This tool helps Language Models understand and generate high-quality Go code following established patterns and conventions.

Math-MCP

Um servidor de Protocolo de Contexto de Modelo que fornece funções matemáticas e estatísticas básicas para LLMs, permitindo que eles realizem cálculos numéricos precisos através de uma API simples.

Data.gov MCP Server

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Semantic Scholar MCP Server

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repo-to-txt-mcp

Servidor MCP para analisar e converter repositórios Git em arquivos de texto para contexto de LLM.

Google Home MCP Server

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mcp-server-restart

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better-auth-mcp-server MCP Server

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HANA Cloud MCP Server

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Filesystem MCP Server

Servidor MCP de Sistema de Arquivos Aprimorado

MCP Mistral OCR

Okay, I understand. You want to use the Mistral OCR API (which is a paid service) to perform OCR on images or PDFs, either from local files or from URLs, and you want to do this programmatically. To give you the best possible guidance, I need a little more information about what you're trying to achieve and your technical background. However, I can provide a general outline and some code snippets to get you started. **General Outline:** 1. **Sign up for a Mistral OCR API Account:** You'll need to create an account and obtain an API key from Mistral. This is essential for authenticating your requests. Refer to the Mistral OCR API documentation for their sign-up process and pricing. 2. **Choose a Programming Language:** Python is a popular choice for working with APIs due to its ease of use and available libraries. I'll provide examples in Python. 3. **Install Necessary Libraries:** You'll likely need the `requests` library for making HTTP requests to the API. You might also need libraries like `PIL` (Pillow) for image manipulation or `PyPDF2` for working with PDFs. 4. **Implement the OCR Logic:** This involves: * Reading the image or PDF data (either from a local file or downloading from a URL). * Constructing the API request with the appropriate headers and data (including your API key and the image/PDF data). * Sending the request to the Mistral OCR API endpoint. * Handling the API response (checking for errors, parsing the OCR'd text). 5. **Error Handling:** Implement robust error handling to deal with potential issues like network errors, invalid API keys, incorrect file formats, or API rate limits. **Python Example (Illustrative - Requires Adaptation):** ```python import requests import io # For handling in-memory file-like objects from PIL import Image # For image handling (optional) #from PyPDF2 import PdfReader # For PDF handling (optional) # Replace with your actual Mistral OCR API key and endpoint API_KEY = "YOUR_MISTRAL_API_KEY" API_ENDPOINT = "YOUR_MISTRAL_OCR_API_ENDPOINT" # Check Mistral's documentation def ocr_image_from_url(image_url): """Performs OCR on an image from a URL using the Mistral OCR API.""" try: response = requests.get(image_url, stream=True) response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx) # Open the image using Pillow (PIL) image = Image.open(response.raw) # Convert the image to bytes (e.g., JPEG or PNG) img_byte_arr = io.BytesIO() image.save(img_byte_arr, format='JPEG') # Or 'PNG' img_byte_arr = img_byte_arr.getvalue() headers = { "Authorization": f"Bearer {API_KEY}", # Or however Mistral requires authentication } files = { "image": ("image.jpg", img_byte_arr, "image/jpeg"), # Adjust filename and MIME type } api_response = requests.post(API_ENDPOINT, headers=headers, files=files) api_response.raise_for_status() result = api_response.json() # Assuming the API returns JSON return result.get("text") # Adjust based on the API's response format except requests.exceptions.RequestException as e: print(f"Error during request: {e}") return None except Exception as e: print(f"An error occurred: {e}") return None def ocr_image_from_file(image_path): """Performs OCR on a local image file.""" try: with open(image_path, "rb") as image_file: headers = { "Authorization": f"Bearer {API_KEY}", # Or however Mistral requires authentication } files = { "image": (image_path, image_file, "image/jpeg"), # Adjust MIME type } api_response = requests.post(API_ENDPOINT, headers=headers, files=files) api_response.raise_for_status() result = api_response.json() # Assuming the API returns JSON return result.get("text") # Adjust based on the API's response format except FileNotFoundError: print(f"Error: File not found at {image_path}") return None except requests.exceptions.RequestException as e: print(f"Error during request: {e}") return None except Exception as e: print(f"An error occurred: {e}") return None def ocr_pdf_from_url(pdf_url): """Performs OCR on a PDF from a URL using the Mistral OCR API.""" try: response = requests.get(pdf_url, stream=True) response.raise_for_status() headers = { "Authorization": f"Bearer {API_KEY}", # Or however Mistral requires authentication } files = { "pdf": ("document.pdf", response.raw, "application/pdf"), # Adjust filename and MIME type } api_response = requests.post(API_ENDPOINT, headers=headers, files=files) api_response.raise_for_status() result = api_response.json() # Assuming the API returns JSON return result.get("text") # Adjust based on the API's response format except requests.exceptions.RequestException as e: print(f"Error during request: {e}") return None except Exception as e: print(f"An error occurred: {e}") return None def ocr_pdf_from_file(pdf_path): """Performs OCR on a local PDF file.""" try: with open(pdf_path, "rb") as pdf_file: headers = { "Authorization": f"Bearer {API_KEY}", # Or however Mistral requires authentication } files = { "pdf": (pdf_path, pdf_file, "application/pdf"), # Adjust MIME type } api_response = requests.post(API_ENDPOINT, headers=headers, files=files) api_response.raise_for_status() result = api_response.json() # Assuming the API returns JSON return result.get("text") # Adjust based on the API's response format except FileNotFoundError: print(f"Error: File not found at {pdf_path}") return None except requests.exceptions.RequestException as e: print(f"Error during request: {e}") return None except Exception as e: print(f"An error occurred: {e}") return None # Example usage: # image_url = "https://example.com/image.jpg" # extracted_text = ocr_image_from_url(image_url) image_path = "path/to/your/image.jpg" # Replace with your image path extracted_text = ocr_image_from_file(image_path) # pdf_url = "https://example.com/document.pdf" # extracted_text = ocr_pdf_from_url(pdf_url) # pdf_path = "path/to/your/document.pdf" # Replace with your PDF path # extracted_text = ocr_pdf_from_file(pdf_path) if extracted_text: print("Extracted Text:\n", extracted_text) else: print("OCR failed.") ``` **Important Considerations and Next Steps:** * **Mistral OCR API Documentation:** The most important thing is to **carefully read the Mistral OCR API documentation.** This will tell you: * The exact API endpoint URL. * How to authenticate your requests (the format of the `Authorization` header). * The expected format for sending image/PDF data (e.g., as a file upload, as base64 encoded data, etc.). * The format of the API response (how the OCR'd text is returned). * Any limitations on file size, image formats, or API usage. * **Error Handling:** The example code includes basic error handling, but you should expand this to handle specific API error codes and implement retry logic if necessary. * **File Formats:** Ensure that the image or PDF is in a format supported by the Mistral OCR API. You might need to convert files to a supported format before sending them. * **Rate Limiting:** Be aware of any rate limits imposed by the Mistral OCR API and implement appropriate delays or throttling in your code to avoid exceeding those limits. * **PDF Handling:** For PDFs, you might need to extract individual pages as images if the API doesn't directly support multi-page PDFs. The `PyPDF2` library can help with this. * **Portuguese Language Support:** Check if the Mistral OCR API has specific options or parameters for specifying the language of the text in the image/PDF. This can improve the accuracy of the OCR results. Look for a `language` parameter or similar in the API documentation. **To help me give you more specific advice, please tell me:** * **What programming language are you using?** (If not Python) * **Do you have a specific use case in mind?** (e.g., processing a large batch of documents, real-time OCR, etc.) * **Have you already tried anything?** (If so, what were the results?) * **Can you share a link to the Mistral OCR API documentation?** (This is crucial for accurate code.) Once I have this information, I can provide more tailored code examples and guidance.

AISDK MCP Bridge

Bridge package enabling seamless integration between Model Context Protocol (MCP) servers and AI SDK tools. Supports multiple server types, real-time communication, and TypeScript.

MailchimpMCP

Some utilities for developing an MCP server for the Mailchimp API

MCP Ayd Server

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MCP Server Playground

Um servidor MCP baseado em TypeScript, projetado para experimentação e integração com o Calude Desktop e o Cursor IDE, oferecendo um ambiente modular para estender as capacidades do servidor.

MCP 服务器示例

A MCP Server FastDemo with webui

GraphQL MCP Server

Um servidor TypeScript que fornece à IA Claude acesso contínuo a qualquer API GraphQL através do Protocolo de Contexto de Modelo.

Data Visualization MCP Server

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Deep Research MCP Server 🚀

MCP Deep Research Server using Gemini creating a Research AI Agent

Clover MCP (Model Context Protocol) Server

Permite que agentes de IA acessem e interajam com dados de comerciantes, inventário e pedidos do Clover por meio de um servidor MCP autenticado por OAuth seguro.

MCP with Gemini Tutorial

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