发现优秀的 MCP 服务器

Apache Doris MCP Server

Apache Doris 和 VeloDB 的 MCP 服务器

ai-dev-mcp-server-test-1743931248063

AI 驱动的开发 MCP 服务器 (AI qūdòng de kāifā MCP fúwùqì)

Ditto MCP Server

Enables secure execution of Ditto DQL (Data Query Language) queries over HTTPS with safety checks and capability gating. Supports parameterized queries, health checks, and configuration management for Ditto database operations.

CodeSeeker-MCP

Advanced code search and transformation MCP server for AI assistants. Combines ugrep's speed with intelligent replace capabilities, dry-run previews, and language-aware refactoring across 11 tools.

test-repo-from-custom-mcp

由自定义 MCP 服务器创建的测试存储库

auto-dev-next

使用 Compose UI 和 MCP 控制后端服务器的 AutoDev Next。

Trello MCP Server with Python

一个强大的 MCP 服务器，用于通过 AI 主机与 Trello 看板、列表和卡片进行交互。

GridStack MCP Server

Provides comprehensive access to GridStack.js functionality for building dynamic dashboard layouts and responsive drag-and-drop grid systems. Includes 26+ tools for widget management, layout control, and serialization with support for React, Vue, and modern CSS frameworks.

MCP AI Infra Real Time Agent

开发了一种基于 MCP 的 AI 基础设施，该基础设施能够为 Claude 和 Cursor 等 AI 客户端提供实时工具执行、结构化知识检索和动态代理交互。

Discord MCP Server

一个 MCP 服务器，它允许 Claude 通过提供发送/读取消息以及通过 Discord API 管理服务器资源的工具来与 Discord 互动。

Gemini MCP Server

一个 TypeScript 服务器，通过模型上下文协议将 Google 的 Gemini Pro 模型与 Claude Desktop 集成，从而允许 Claude 用户访问 Gemini 的文本生成能力。

Arre Ankit_notion Mcp Server

镜子 (jìng zi)

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)

Redash MCP Server

Enables interaction with Redash instances through a standardized interface, allowing users to execute SQL queries, manage data sources, and retrieve query results using natural language.

Wegene Assistant MCP Server

利用大型语言模型分析用户的微基因（WeGene）基因检测报告，通过自定义 URI 方案提供对报告数据的访问，并通过 OAuth 认证和 API 使用实现个人资料和报告管理。

@ragrabbit/mcp

访问 RagRabbit 开源 AI 站点搜索索引的任何文档。

ChillMCP

Enables AI agents to take breaks and manage stress levels through various休息 activities like coffee breaks, Netflix watching, and bathroom breaks. Features a boss alert system that adds realistic workplace tension to the break-taking experience.

NEAR MCP

通过 MCP 调用与 NEAR 区块链交互

README Generator MCP Server

Enables LLMs to automatically analyze project structures, detect technologies, and generate comprehensive, professional README documentation files with proper formatting, badges, and sections.

Pipedrive MCP Server by CData

This project builds a read-only MCP server. For full read, write, update, delete, and action capabilities and a simplified setup, check out our free CData MCP Server for Pipedrive (beta): https://www.cdata.com/download/download.aspx?sku=KDZK-V&type=beta

Backlog MCP Server

通过模型上下文协议将 Backlog 项目管理与 Claude 集成，从而可以通过自然语言交互访问项目、问题和 Wiki 页面。

Directus MCP Server

An extension that implements the Model Context Protocol for Directus, enabling AI tools and LLMs to interact with Directus content through natural language for content editing, data analysis, and asset management.

Korean Company Information MCP Server

Enables AI agents to query Korean listed companies' financial statements, public disclosures, executive information, and shareholder structures in real-time using the DART API.

Microsoft Graph MCP Server

A Model Context Protocol server that connects to Microsoft Graph API, allowing AI assistants to query and access data from Microsoft Entra ID (formerly Azure Active Directory).

MCP-servers

Okay, here are a few options for translating "a version focused on enabling web browsing from the Claude app" into Chinese, with slightly different nuances depending on the specific emphasis you want to convey: **Option 1 (Most straightforward and common):** * **中文：** 一个专注于在 Claude 应用内启用网页浏览的版本 * **Pinyin:** Yī gè zhuānzhù yú zài Claude yìngyòng nèi qǐyòng wǎngyè liúlǎn de bǎnběn * **Explanation:** This is a direct and clear translation. "专注于 (zhuānzhù yú)" means "focused on," "在...内 (zài...nèi)" means "within/inside," "启用 (qǐyòng)" means "enable/activate," and "网页浏览 (wǎngyè liúlǎn)" means "web browsing." **Option 2 (Emphasizing the capability/functionality):** * **中文：** 一个旨在让 Claude 应用具备网页浏览功能的版本 * **Pinyin:** Yī gè zhǐzài ràng Claude yìngyòng jùbèi wǎngyè liúlǎn gōngnéng de bǎnběn * **Explanation:** This version uses "旨在 (zhǐzài)" which means "aims to," and "具备...功能 (jùbèi...gōngnéng)" which means "possesses the function/capability of..." It highlights the *functionality* being added. **Option 3 (More concise and slightly more technical):** * **中文：** 一个支持 Claude 应用内网页浏览的版本 * **Pinyin:** Yī gè zhīchí Claude yìngyòng nèi wǎngyè liúlǎn de bǎnběn * **Explanation:** This is shorter and uses "支持 (zhīchí)" which means "supports." It's a bit more technical and implies that the version *allows* web browsing within the Claude app. **Option 4 (Focus on integration):** * **中文：** 一个将网页浏览功能集成到 Claude 应用中的版本 * **Pinyin:** Yī gè jiāng wǎngyè liúlǎn gōngnéng jíchéng dào Claude yìngyòng zhōng de bǎnběn * **Explanation:** This emphasizes the *integration* of web browsing into the Claude app. "集成 (jíchéng)" means "integrate." **Which one is best?** * **Option 1** is generally a good choice for most situations. It's clear and easy to understand. * Choose **Option 2** if you want to emphasize the *capability* being added. * Choose **Option 3** if you want a more concise and slightly technical description. * Choose **Option 4** if you want to highlight the *integration* aspect. Therefore, I recommend **Option 1** unless you have a specific reason to emphasize functionality, support, or integration.

EPICS MCP Server

Enables interaction with EPICS (Experimental Physics and Industrial Control System) process variables through natural language. Supports reading PV values, setting PV values, and retrieving detailed PV information for monitoring and controlling hardware or software parameters.

Zapmail MCP Server

Enables AI assistants to interact with the Zapmail API through natural language commands for domain management, mailbox operations, and exports to third-party platforms like Reachinbox and Instantly. Provides complete coverage of 46+ Zapmail tools with dynamic API integration and multi-workspace support.

Notion MCP Server

一个允许在 Notion MCP 中使用块、切换等功能的服务器。