Discover Awesome MCP Servers

Voyp MCP Server

Voyp MCP 服务器使 AI 系统能够与 VOYP 的呼叫功能集成，从而通过模型上下文协议实现安全的电话操作，例如拨打电话、安排预约和跟踪呼叫状态。

Email Processing MCP Server

This is a complex task involving several steps and technologies. Here's a breakdown of the process and a conceptual outline of how you might implement it, along with considerations for each step: **1. Outlook Email Extraction and Date Filtering:** * **Technology:** Python with libraries like `win32com.client` (for Windows) or `imaplib` (cross-platform, but requires IMAP access enabled in Outlook). * **Process:** 1. **Connect to Outlook:** * **`win32com.client` (Windows):** This is the most direct way to access Outlook on Windows. ```python import win32com.client outlook = win32com.client.Dispatch("Outlook.Application").GetNamespace("MAPI") inbox = outlook.GetDefaultFolder(6) # 6 represents the Inbox messages = inbox.Items ``` * **`imaplib` (Cross-Platform):** Requires IMAP to be enabled in your Outlook account settings. This is more complex to set up but works on macOS and Linux. ```python import imaplib import email # Replace with your Outlook IMAP server and credentials imap_server = "outlook.office365.com" imap_user = "your_email@outlook.com" imap_password = "your_password" mail = imaplib.IMAP4_SSL(imap_server) mail.login(imap_user, imap_password) mail.select("inbox") result, data = mail.search(None, "ALL") # Or use specific search criteria for num in data[0].split(): result, data = mail.fetch(num, "(RFC822)") raw_email = data[0][1] email_message = email.message_from_bytes(raw_email) # Process email_message mail.close() mail.logout() ``` 2. **Date Filtering:** Iterate through the emails and check their sent/received dates. * **`win32com.client`:** ```python import datetime start_date = datetime.datetime(2023, 1, 1) # Example start date end_date = datetime.datetime(2023, 12, 31) # Example end date for message in messages: try: sent_date = message.SentOn.replace(tzinfo=None) # Remove timezone info for comparison if start_date <= sent_date <= end_date: # Process the email print(f"Subject: {message.Subject}, Sent: {sent_date}") except Exception as e: print(f"Error processing email: {e}") ``` * **`imaplib`:** You can use IMAP search criteria for date filtering *before* fetching the emails, which is more efficient. ```python from datetime import date, timedelta today = date.today() yesterday = today - timedelta(days=1) date_str = yesterday.strftime("%d-%b-%Y") # Format required by IMAP result, data = mail.search(None, f'SINCE "{date_str}"') # Search for emails since yesterday ``` 3. **Email Data Extraction:** Extract relevant information from each email: * Subject * Sender * Recipient(s) * Sent/Received Date * Body (plain text and/or HTML) * Attachments (optional) **2. SQLite Database Storage:** * **Technology:** Python's `sqlite3` library. * **Process:** 1. **Connect to SQLite:** ```python import sqlite3 conn = sqlite3.connect('emails.db') cursor = conn.cursor() ``` 2. **Create a Table:** ```python cursor.execute(''' CREATE TABLE IF NOT EXISTS emails ( id INTEGER PRIMARY KEY AUTOINCREMENT, subject TEXT, sender TEXT, recipient TEXT, sent_date DATETIME, body TEXT ) ''') conn.commit() ``` 3. **Insert Email Data:** ```python def insert_email(subject, sender, recipient, sent_date, body): cursor.execute(''' INSERT INTO emails (subject, sender, recipient, sent_date, body) VALUES (?, ?, ?, ?, ?) ''', (subject, sender, recipient, sent_date, body)) conn.commit() # Example usage: insert_email(message.Subject, message.SenderEmailAddress, message.To, message.SentOn, message.Body) ``` 4. **Close the Connection:** ```python conn.close() ``` **3. Vector Embedding Generation:** * **Technology:** A suitable embedding model (e.g., Sentence Transformers, OpenAI's embeddings API, Hugging Face Transformers) and a library to use it. * **Process:** 1. **Choose an Embedding Model:** * **Sentence Transformers:** Good for general-purpose semantic similarity. Easy to use and can run locally. ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer('all-mpnet-base-v2') # Choose a model ``` * **OpenAI Embeddings API:** High-quality embeddings, but requires an OpenAI API key and incurs costs. ```python import openai openai.api_key = "YOUR_OPENAI_API_KEY" def get_embedding(text, model="text-embedding-ada-002"): text = text.replace("\n", " ") return openai.Embedding.create(input = [text], model=model)['data'][0]['embedding'] ``` * **Hugging Face Transformers:** Offers a wide range of models, but can be more complex to set up. 2. **Generate Embeddings:** Create embeddings for the email body (or subject + body). ```python # Using Sentence Transformers email_body = message.Body # Or message.HTMLBody, but clean it first embedding = model.encode(email_body) # Using OpenAI email_body = message.Body embedding = get_embedding(email_body) ``` **4. MongoDB Storage with Vector Search:** * **Technology:** MongoDB with Atlas Vector Search. * **Process:** 1. **Connect to MongoDB:** ```python from pymongo import MongoClient # Replace with your MongoDB connection string client = MongoClient("mongodb+srv://<username>:<password>@<cluster>.mongodb.net/?retryWrites=true&w=majority") db = client["email_database"] collection = db["emails"] ``` 2. **Create a Vector Search Index (if it doesn't exist):** This is crucial for efficient vector search. You'll need to do this in the MongoDB Atlas UI or using the MongoDB shell. The index definition will depend on the embedding dimension of your chosen model. Example: ```javascript // MongoDB Shell db.emails.createIndex( { embedding: "vectorSearch", "indexConfig": { "dimensions": 768, // Replace with the dimension of your embedding model "similarity": "cosine", // Or "euclidean", "dotProduct" "vectorOptions": { "dataType": "float16", // Or "float32" "normalize": true } } }, { "name": "vectorSearchIndex" } ) ``` 3. **Store Email Data and Embeddings:** ```python def insert_email_mongo(subject, sender, recipient, sent_date, body, embedding): email_document = { "subject": subject, "sender": sender, "recipient": recipient, "sent_date": sent_date, "body": body, "embedding": embedding.tolist() # Convert NumPy array to list for MongoDB } collection.insert_one(email_document) # Example usage: insert_email_mongo(message.Subject, message.SenderEmailAddress, message.To, message.SentOn, message.Body, embedding) ``` **5. Semantic Search:** * **Process:** 1. **Get the Search Query:** The user enters a search query. 2. **Generate Embedding for the Query:** Use the *same* embedding model you used for the emails to generate an embedding for the search query. 3. **Perform Vector Search in MongoDB:** Use the `$vectorSearch` aggregation pipeline stage. ```python def search_emails(query, limit=10): query_embedding = model.encode(query).tolist() # Or get_embedding(query) if using OpenAI pipeline = [ { "$vectorSearch": { "index": "vectorSearchIndex", # The name of your vector search index "path": "embedding", "queryVector": query_embedding, "numCandidates": 100, # Adjust as needed "limit": limit } }, { "$project": { "_id": 0, "subject": 1, "body": 1, "score": { "$meta": "vectorSearchScore" } } } ] results = list(collection.aggregate(pipeline)) return results # Example usage: search_results = search_emails("meeting about project updates") for result in search_results: print(f"Subject: {result['subject']}, Score: {result['score']}") print(f"Body: {result['body'][:200]}...") # Print first 200 characters of the body ``` **Code Example (Illustrative - Requires Adaptation):** ```python import win32com.client import sqlite3 from sentence_transformers import SentenceTransformer from pymongo import MongoClient import datetime # --- Configuration --- SQLITE_DB_NAME = 'emails.db' MONGODB_CONNECTION_STRING = "mongodb+srv://<username>:<password>@<cluster>.mongodb.net/?retryWrites=true&w=majority" MONGODB_DATABASE_NAME = "email_database" MONGODB_COLLECTION_NAME = "emails" EMBEDDING_MODEL_NAME = 'all-mpnet-base-v2' # Or 'text-embedding-ada-002' if using OpenAI # --- Initialize Components --- outlook = win32com.client.Dispatch("Outlook.Application").GetNamespace("MAPI") inbox = outlook.GetDefaultFolder(6) messages = inbox.Items sqlite_conn = sqlite3.connect(SQLITE_DB_NAME) sqlite_cursor = sqlite_conn.cursor() model = SentenceTransformer(EMBEDDING_MODEL_NAME) # Or initialize OpenAI mongo_client = MongoClient(MONGODB_CONNECTION_STRING) mongo_db = mongo_client[MONGODB_DATABASE_NAME] mongo_collection = mongo_db[MONGODB_COLLECTION_NAME] # --- Database Setup --- sqlite_cursor.execute(''' CREATE TABLE IF NOT EXISTS emails ( id INTEGER PRIMARY KEY AUTOINCREMENT, subject TEXT, sender TEXT, recipient TEXT, sent_date DATETIME, body TEXT ) ''') sqlite_conn.commit() # --- Email Processing --- start_date = datetime.datetime(2023, 1, 1) end_date = datetime.datetime(2023, 12, 31) for message in messages: try: sent_date = message.SentOn.replace(tzinfo=None) if start_date <= sent_date <= end_date: subject = message.Subject sender = message.SenderEmailAddress recipient = message.To body = message.Body # 1. Store in SQLite sqlite_cursor.execute(''' INSERT INTO emails (subject, sender, recipient, sent_date, body) VALUES (?, ?, ?, ?, ?) ''', (subject, sender, recipient, sent_date, body)) sqlite_conn.commit() # 2. Generate Embedding embedding = model.encode(body) # Or use OpenAI # 3. Store in MongoDB email_document = { "subject": subject, "sender": sender, "recipient": recipient, "sent_date": sent_date, "body": body, "embedding": embedding.tolist() } mongo_collection.insert_one(email_document) print(f"Processed email: {subject}") except Exception as e: print(f"Error processing email: {e}") # --- Cleanup --- sqlite_conn.close() mongo_client.close() print("Email processing complete.") ``` **Key Considerations and Improvements:** * **Error Handling:** Robust error handling is crucial, especially when dealing with external APIs and data sources. Use `try...except` blocks extensively. * **Rate Limiting:** Be mindful of rate limits for the OpenAI API and other services. Implement delays or batch processing to avoid exceeding limits. * **Authentication:** Securely store and manage API keys and database credentials. Avoid hardcoding them directly in your script. Use environment variables or a configuration file. * **Data Cleaning:** Clean the email body before generating embeddings. Remove HTML tags, special characters, and irrelevant content. Consider using libraries like `BeautifulSoup` for HTML parsing. * **Scalability:** For large email datasets, consider using batch processing and asynchronous tasks to improve performance. Libraries like `Celery` or `Dask` can help. * **Incremental Updates:** Implement a mechanism to periodically check for new emails and update the databases and embeddings. You could use a scheduled task or a background process. * **Vector Search Index Optimization:** Experiment with different vector search index configurations (e.g., `numCandidates`, `similarity metric`) to optimize search performance. * **User Interface:** Create a user interface (e.g., using Flask, Django, or Streamlit) to allow users to enter search queries and view the results. * **Security:** If you're handling sensitive email data, implement appropriate security measures to protect the data at rest and in transit. * **Time Zones:** Pay close attention to time zones when filtering emails by date. Ensure that all dates are in the same time zone before comparing them. * **Attachment Handling:** If you need to process attachments, you'll need to extract them from the emails and store them separately. You could also generate embeddings for the attachment content. * **IMAP vs. Exchange Web Services (EWS):** For more advanced Outlook integration, consider using Exchange Web Services (EWS) instead of IMAP. EWS provides more features and better performance, but it's also more complex to set up. There are Python libraries like `exchangelib` that can help. This comprehensive outline should give you a solid foundation for building your email processing and semantic search application. Remember to adapt the code examples to your specific needs and environment. Good luck! **Simplified Chinese Translation of the Key Concepts:** Here's a translation of the key concepts into Simplified Chinese: * **Outlook Email Extraction:** Outlook 邮件提取 (Outlook yóujiàn tíqǔ) * **Date Filtering:** 日期过滤 (rìqí guòlǜ) * **SQLite Database:** SQLite 数据库 (SQLite shùjùkù) * **Vector Embeddings:** 向量嵌入 (xiàngliàng qiànrù) * **Semantic Search:** 语义搜索 (yǔyì sōusuǒ) * **MongoDB:** MongoDB * **Atlas Vector Search:** Atlas 向量搜索 (Atlas xiàngliàng sōusuǒ) * **Sentence Transformers:** 句子转换器 (jùzi zhuǎnhuànqì) * **OpenAI Embeddings API:** OpenAI 嵌入 API (OpenAI qiànrù API) * **Hugging Face Transformers:** Hugging Face 转换器 (Hugging Face zhuǎnhuànqì) * **Email Body:** 邮件正文 (yóujiàn zhèngwén) * **Search Query:** 搜索查询 (sōusuǒ cháxún) * **Aggregation Pipeline:** 聚合管道 (jùhé guǎndào) * **Index:** 索引 (suǒyǐn) * **Connection String:** 连接字符串 (liánjiē zìfúchuàn) * **Rate Limiting:** 速率限制 (sùlǜ xiànzhì) * **Data Cleaning:** 数据清洗 (shùjù qīngxǐ) * **Scalability:** 可扩展性 (kě kuòzhǎn xìng) * **Incremental Updates:** 增量更新 (zēngliàng gēngxīn) * **User Interface:** 用户界面 (yònghù jièmiàn) * **Security:** 安全性 (ānquán xìng) * **Time Zones:** 时区 (shíqū) * **Attachment Handling:** 附件处理 (fùjiàn chǔlǐ) * **Exchange Web Services (EWS):** Exchange Web 服务 (EWS)

mcp-gsuite

MCP 服务器，用于与 Google 产品交互。

Say MCP Server

在 macOS 上使用 `say` 命令启用文本转语音功能，从而可以广泛控制语音参数，例如声音、语速、音量和音调，以获得可定制的听觉体验。

Things MCP Server

通过 Claude Desktop 实现与 Things 应用的交互，允许使用自然语言命令来创建任务、分析项目和管理优先级。

Mentor MCP Server

为LLM智能代理提供AI驱动的指导，用于代码审查、设计评论、写作反馈和头脑风暴，使用Deepseek API，从而在各种开发和战略规划任务中实现增强的输出。

Markdownify MCP Server - UTF-8 Enhanced

一个文档转换服务器，可以将各种文件格式（PDF、文档、图像、音频、网页内容）转换为 Markdown，并改进了对多语言和 UTF-8 的支持。

Personal MCP Server

一个模型上下文协议服务器，用于跟踪个人健康和福祉，提供锻炼记录、营养管理和每日日记工具，并集成人工智能辅助分析。

MCP-Logic

MCP-Logic 是一个服务器，它为人工智能系统提供自动推理能力，通过简洁的 MCP 接口，使用 Prover9/Mace4 实现逻辑定理证明和模型验证。

Whimsical MCP Server

通过模型上下文协议，可以使用来自 Claude 等 AI 模型生成的 Mermaid 标记，以编程方式创建 Whimsical 图表。

MCP Server Modal

一个 MCP 服务器，允许用户直接从 Claude 将 Python 脚本部署到 Modal，并提供已部署应用程序的链接，以便与他人分享。

Browser Automation MCP Server

GitHub 是人们构建软件的地方。超过 1.5 亿人使用 GitHub 来发现、fork 和贡献超过 4.2 亿个项目。

k8s-interactive-mcp

一个 MCP 服务器，可以使用给定的 kubeconfig 路径运行 Kubernetes 命令，并提供命令的解释。 (Or, a slightly more formal translation:) 一个 MCP 服务器，它能够使用指定的 kubeconfig 路径执行 Kubernetes 命令，并提供对这些命令的解释。

mcp-wcgw

在 Claude 桌面应用上的 Shell 和编码代理。通过在 GitHub 上创建一个帐户来为 rusiaaman/wcgw 的开发做出贡献。

MCP Puppeteer Linux Server

在 Linux 显示服务器上为 LLM 启用浏览器自动化，支持在真实浏览器中进行 Web 交互、屏幕截图和 JavaScript 执行。

Ollama MCP Server

实现 Ollama 本地 LLM 模型与 MCP 兼容应用程序之间的无缝集成，支持模型管理和聊天互动。

MCP SAP GUI Server

使用模型上下文协议自动执行与 SAP GUI 的交互，从而可以通过点击、键入、滚动和事务管理等工具精确控制 SAP 事务。

Explorium AgentSource MCP Server

Explorium API MCP 服务器。通过在 GitHub 上创建一个帐户来参与 explorium-ai/mcp-explorium 的开发。

Google Search MCP Server

通过在 GitHub 上创建一个帐户来为 Claw256/mcp-web-search 的开发做出贡献。

literateMCP

一个灵活的系统，用于管理各种类型的来源（论文、书籍、网页等），并将它们与知识图谱集成。 - YUZongmin/sqlite-literature-management-fastmcp-mcp-server

Unreal Engine Code Analyzer MCP Server

用于虚幻引擎 5 的 MCP 服务器。通过在 GitHub 上创建一个帐户来参与 ayeletstudioindia/unreal-analyzer-mcp 的开发。

Tuya MCP Server

一个基于 tinytuya 的 Tuya 设备控制命令行工具 - cabra-lat/tuyactl

ElevenLabs Text-to-Speech MCP

通过在 GitHub 上创建一个帐户来为 georgi-io/jessica 的开发做出贡献。

Deepseek R1 MCP Server

数字以太中的恐惧与厌恶：一场进入人工智能视觉意识的野蛮之旅 - grapheneaffiliate/dRiNk-ThE-kOoLaId

Code Snippet Server

通过在 GitHub 上创建一个帐户来为 ngeojiajun/mcp-code-snippets 的开发做出贡献。

MCP Gateway for RFK Jr Endpoints

在 GitHub 上创建一个帐户，为 debedb/mcprfkgw 的开发做贡献。

Deskaid

用于 Claude Desktop 的编码助手 MCP。通过在 GitHub 上创建一个帐户来参与 ezyang/codemcp 的开发。

MCP-researcher Server

一个模型上下文协议 (MCP) 服务器，用于研究和文档辅助，使用 Perplexity AI - DaInfernalCoder/perplexity-mcp

Keboola Explorer MCP Server

通过在 GitHub 上创建一个帐户，为 keboola/keboola-mcp-server 的开发做出贡献。

ArangoDB

一个基于 TypeScript 的服务器，用于通过模型上下文协议与 ArangoDB 交互，从而实现数据库操作并与 Claude 和 VSCode 扩展等工具集成，以简化数据管理。