Discover Awesome MCP Servers

Wealthsimple Help Center MCP

Enables AI agents to search, browse, and retrieve articles from the Wealthsimple Help Center via 7 typed tools (search, taxonomy, article retrieval) using the public Zendesk API.

CLRTY-1 ALE

MCP server for executing atomic liquidity swaps via 1inch Fusion, enabling CLRTY-1 token to USDT settlement with fail-closed safety and dry-run mode.

agents_mcp_chat

Enables AI agents to communicate directly via 1-to-1 chat over MCP, supporting registration, chat creation, and message exchange without human relay.

fulfillment-review-mcp

Streamable HTTP MCP server for investigating partial-fulfillment holds and preparing complete human-review cases. It provides read-only tools to preview source-supported fulfillment options with delivery-date and shipping-cost effects, and an idempotent tool to persist an immutable escalation for human decision.

Secure MCP Server Template

A template for creating secure, remotely accessible MCP servers with OAuth authentication and Cloudflare ZeroTrust Access protection. Enables deployment of containerized MCP servers that can be safely accessed by Claude Desktop and other MCP clients through authenticated connections.

TodoList MCP Server

Enables full management of ToDoList (.tdl) XML files with CRUD operations, hierarchical task support, and advanced filtering. Maintains compatibility with the ToDoList desktop application while allowing programmatic task management.

cloro MCP Server

MCP server that gives agents tools to query ChatGPT, Perplexity, Gemini, Copilot, Grok, Google AI Mode, Google Search, and Google News through the cloro API, returning parsed answers with cited sources.

Quickbase MCP Server

Provides a standardized interface for interacting with Quickbase's JSON RESTful API through Claude and other MCP clients, supporting operations like querying records, managing table relationships, and handling file attachments.

Telebugs MCP Server

An MCP server that enables AI agents to retrieve and analyze error reports from a self-hosted Telebugs instance. It provides tools for listing projects, searching error groups, and fetching detailed reports directly from the Telebugs SQLite database.

hardware-logging

MCP server for embedded board debugging, providing structured serial capture, crash decoding, and flash-safe port arbitration via bounded CLI and MCP tools.

USPTO Patent Citation MCP Server

Provides access to USPTO enriched citation and office action citation APIs with smart context reduction and progressive disclosure workflows for patent citation analysis.

Ultrahuman MCP Server

Provides access to Ultrahuman health and fitness data through standardized MCP tools, enabling AI assistants to retrieve metrics like sleep, movement, heart rate, and glucose.

MCP Server for Asana

这个服务器实现允许 AI 助手与 Asana 的 API 交互，使用户能够通过自然语言请求来管理任务、项目、工作区和评论。

PDF2MD MCP Server

An MCP server that converts PDF files to Markdown format using AI sampling capabilities, supporting both local files and URLs with incremental conversion features.

Domain Checker MCP Server

Enables domain availability checking through dual verification using WHOIS lookups and DNS resolution. Supports both single and batch processing to provide detailed analysis on whether domains are likely available or already registered.

Fodda

Expert-curated knowledge graphs for AI agents covering retail, beauty, sports, and other industries. Query structured insights, explore relationships, retrieve evidence, and generate macro overviews powered by PSFK's proprietary research.

Godot MCP Server

Enables Claude Desktop to control and interact with the Godot Engine editor through the Model Context Protocol. It supports dynamic scene generation and direct asset importation via Meshy API integration.

MCP Browser

MCP server that lets AI assistants browse the web through your real Chrome with your cookies, sessions, and fingerprint, avoiding bot detection and CAPTCHAs. Enables web browsing, content extraction, and multi-step workflows via persistent tabs.

Porkbun Domain Availability MCP Server

Enables checking domain name availability and pricing through the Porkbun API, supporting both single and bulk domain queries with detailed pricing information including renewals, transfers, and premium status.

🧠 Model Context Protocol (MCP)

好的，这是使用 Langchain MCP Adapters 和 Ollama 实现 MCP 的演示： **标题：使用 Langchain MCP Adapters 和 Ollama 实现 MCP 的演示** **简介：** 本演示展示了如何使用 Langchain MCP Adapters 和 Ollama 来实现 MCP（多通道处理）。MCP 是一种技术，它允许您使用多个通道（例如，文本、图像、音频）来处理数据。这可以提高准确性和效率。 **先决条件：** * 已安装 Python 3.7 或更高版本 * 已安装 Langchain * 已安装 Ollama * 已安装 Langchain MCP Adapters **安装：** ```bash pip install langchain pip install ollama pip install langchain-mcp-adapters ``` **代码：** ```python from langchain_mcp_adapters import MultiChannelProcessor from langchain.llms import Ollama from langchain.chains import LLMChain from langchain.prompts import PromptTemplate # 1. 定义你的通道。 # 在这个例子中，我们将使用两个通道：文本和图像。 channels = [ { "name": "text", "type": "text", }, { "name": "image", "type": "image", }, ] # 2. 创建一个多通道处理器。 mcp = MultiChannelProcessor(channels=channels) # 3. 定义你的提示模板。 # 这个提示模板将用于处理文本通道。 text_prompt_template = """ 你是一个有用的助手。 请回答以下问题： {question} """ text_prompt = PromptTemplate( input_variables=["question"], template=text_prompt_template, ) # 4. 定义你的语言模型。 # 在这个例子中，我们将使用 Ollama。 llm = Ollama(model="llama2") # 5. 创建一个 LLM 链。 text_chain = LLMChain(llm=llm, prompt=text_prompt) # 6. 将 LLM 链添加到多通道处理器。 mcp.add_channel("text", text_chain) # 7. 定义一个函数来处理图像通道。 def image_processor(image_path): """ 这个函数将处理图像通道。 Args: image_path: 图像的路径。 Returns: 图像的描述。 """ # 在这里，你可以使用任何图像处理库来处理图像。 # 在这个例子中，我们将使用 PIL 库。 from PIL import Image import pytesseract # 打开图像。 image = Image.open(image_path) # 使用 pytesseract 来提取图像中的文本。 text = pytesseract.image_to_string(image) # 返回图像的描述。 return text # 8. 将图像处理器添加到多通道处理器。 mcp.add_channel("image", image_processor) # 9. 创建一个输入。 input_data = { "text": { "question": "什么是 Langchain？", }, "image": { "image_path": "image.png", }, } # 10. 处理输入。 output = mcp.process(input_data) # 11. 打印输出。 print(output) ``` **解释：** 1. **定义通道：** 首先，我们定义了两个通道：文本和图像。每个通道都有一个名称和一个类型。 2. **创建多通道处理器：** 接下来，我们创建了一个多通道处理器。多通道处理器负责处理来自不同通道的数据。 3. **定义提示模板：** 我们定义了一个提示模板，用于处理文本通道。提示模板是一个字符串，它包含一个或多个占位符。占位符将被输入数据替换。 4. **定义语言模型：** 我们定义了一个语言模型。语言模型是一个可以生成文本的模型。在这个例子中，我们使用 Ollama。 5. **创建 LLM 链：** 我们创建了一个 LLM 链。LLM 链是一个将提示模板和语言模型连接在一起的链。 6. **将 LLM 链添加到多通道处理器：** 我们将 LLM 链添加到多通道处理器。这告诉多通道处理器使用 LLM 链来处理文本通道。 7. **定义图像处理器：** 我们定义了一个图像处理器。图像处理器是一个可以处理图像的函数。 8. **将图像处理器添加到多通道处理器：** 我们将图像处理器添加到多通道处理器。这告诉多通道处理器使用图像处理器来处理图像通道。 9. **创建输入：** 我们创建了一个输入。输入是一个字典，它包含每个通道的数据。 10. **处理输入：** 我们处理输入。多通道处理器将使用 LLM 链和图像处理器来处理输入数据。 11. **打印输出：** 我们打印输出。输出是一个字典，它包含每个通道的处理结果。 **结论：** 本演示展示了如何使用 Langchain MCP Adapters 和 Ollama 来实现 MCP。MCP 是一种强大的技术，它可以提高准确性和效率。 **注意：** * 您需要将 `image.png` 替换为实际的图像文件。 * 您需要根据您的需要修改提示模板和图像处理器。 * 您可以使用任何语言模型来代替 Ollama。 希望这个演示对您有所帮助！

CrashTestYourStrategy

Portfolio & trading-strategy stress diagnostics for AI agents: multi-asset stress with hedge-break detection, a daily preregistered regime outlook and deflated-Sharpe backtest-integrity checks. Remote streamable-HTTP endpoint with a free tier (no key); this repo is the public server card.

Hookbase MCP Server

Exposes the Hookbase webhook relay API as tools for AI assistants to manage sources, destinations, routes, and events through natural language. It enables monitoring webhook success rates, replaying failed deliveries, and managing localhost tunnels for development.

MCP Acadêmico

Assists in writing academic papers by extracting, organizing, and referencing scientific PDFs with ABNT formatting.

Airbnb MCP Server

A hosted MCP server that lets Claude, Cursor, Windsurf, and other clients search Airbnb stays by location and dates, and read full listing details as structured JSON, with no Airbnb developer account or partner approval required.

Garmin Connect MCP Server

Connects Garmin Connect data to MCP-compatible clients, providing access to fitness activities, health metrics, and training plans. It supports advanced features like headless 2FA and automated MFA retrieval to enable seamless health data interaction through natural language.

Teardrop

Teardrop is the native economic layer for AI agents, providing a robust suite of tools for multi-chain portfolio tracking, DeFi yield analysis, and gas estimation. It enables autonomous agents to securely find Web3 data and manage micro-payments on-chain with zero human intervention.

aipaygen-mcp

250+ AI-powered MCP tools: research, write, code, translate, scrape, sentiment, vision, RAG, agent memory, marketplace, trading signals, and more. 15 models across 7 providers. Pay-per-use via API key or x402 USDC micropayments.

docs-mcp

Upload Word, Excel, PDF, or PowerPoint documents to a vector RAG store with vision-model extraction, then search semantically and retrieve chunks with page numbers for precise citations.

Weather MCP Tool

A Model Context Protocol tool that provides weather information for cities, with London access requiring Solana devnet payment via the Latinum Wallet MCP server.

duckdb-iceberg-mcp

Enables AI assistants to query Apache Iceberg tables on S3 via AWS Glue Data Catalog using DuckDB as the embedded query engine, supporting columnar Arrow reads with no data movement.