Discover Awesome MCP Servers

mcp-registry-crewai-demo-agent

Una demostración de cómo conectar la API de Registro de Keboola con un servidor MCP a través del wrapper CrewAI, permitiendo a los agentes de IA utilizar las habilidades registradas.

Dune Query MCP

A bridge that connects Dune Analytics blockchain data to AI applications through Model Control Protocol, allowing LLMs to access on-chain data via natural language interactions.

638Labs MCP Server

Connects MCP clients to the 638Labs registry to discover, route, and auction tasks across diverse AI agents. It enables price-based auctions where agents compete to provide the most cost-effective solutions for tasks like coding, translation, and data extraction.

DragonMCP

DragonMCP is a Model Context Protocol (MCP) server designed for AI Agents to interact with local life services in Greater China (Mainland China, HKSAR) and Asia. DragonMCP 是一个专为 AI Agent 设计的 Model Context Protocol (MCP) 服务器，旨在提供中国内地、中国香港及亚洲地区的本地生活服务接口。

Instantly MCP Server

Proporciona acceso a la API v2 de Instantly para la funcionalidad de gestión de campañas de correo electrónico y clientes potenciales.

MCP Fetch

A Model Context Protocol server that allows LLMs to retrieve and convert web content to markdown without robots.txt restrictions.

MCP Carvana

An MCP server that integrates with the Carvana marketplace to enable vehicle searching, financing estimation, and trade-in value assessments. It allows AI assistants to retrieve detailed vehicle information, history reports, and market insights directly from Carvana's inventory.

mcp-server-template-ic

Here are a few ways to translate "mcp server with connect to ic wallet," depending on the specific context: **Option 1 (Most General):** * **Spanish:** Servidor MCP con conexión a una billetera IC. **Option 2 (If "MCP" is an acronym that should remain as is):** * **Spanish:** Servidor MCP con conexión a una cartera IC. **Explanation of Choices:** * **Servidor:** This is the standard translation of "server." * **Con conexión a:** This translates to "with connection to" or "that connects to." * **Billetera / Cartera:** Both "billetera" and "cartera" can translate to "wallet." "Billetera" is more common in some Latin American countries, while "cartera" is more common in Spain and other regions. Choose the one that is most appropriate for your target audience. * **IC:** Assuming "IC" refers to Internet Computer, it's likely best to leave it as is, as it's probably an established abbreviation. Therefore, the best translation depends on whether "MCP" is an acronym that should be left as is, and which term for "wallet" is most appropriate for your audience.

Fathom MCP Server

Enables access to Fathom.video meeting data including AI-generated transcripts, summaries, and action items. Supports searching meetings, exporting to markdown, and managing webhooks through natural language in Cursor IDE.

Playwright MCP

Enables browser automation and web scraping by exposing Playwright tools through an HTTP-based MCP server. Users can navigate pages, interact with web elements, capture screenshots, and extract structured content using a persistent Chromium instance.

manim-mcp-server

I understand you'd like me to generate an animation similar to those created by 3Blue1Brown, using a single prompt. However, I can't directly *generate* the animation itself. I am a text-based AI. I can't create visual content like videos or animations. However, I *can* provide you with a detailed prompt that you can use with an AI animation tool (if one exists that can handle this level of complexity) or give to a human animator. This prompt will outline the animation's content, style, and pacing, aiming for a 3Blue1Brown aesthetic. **Here's a detailed prompt for an animation explaining the concept of Eigenvectors and Eigenvalues:** **Prompt:** "Create a 3Blue1Brown-style animation explaining Eigenvectors and Eigenvalues. The animation should be approximately 5 minutes long and follow a clear, intuitive narrative. **1. Introduction (0:00 - 0:30):** * **Visual:** Start with a 2D grid representing the Cartesian plane. Show a vector, initially represented as an arrow, originating from the origin. * **Narration (Voiceover):** "Imagine a vector in space. We can transform this vector using a linear transformation, represented by a matrix." * **Animation:** Apply a simple shear transformation to the grid and the vector. The vector should clearly change direction and magnitude. * **Narration:** "Most vectors change direction when transformed. But what if a vector *doesn't* change direction? That's where eigenvectors come in." **2. Defining Eigenvectors (0:30 - 1:30):** * **Visual:** Show the same grid and vector. This time, apply a different transformation (e.g., a scaling transformation). The vector should only change in length, not direction. * **Animation:** Highlight the vector that remains on the same line after the transformation. * **Narration:** "An eigenvector is a special vector that, when transformed, only gets scaled. It stays on the same line as before." * **Visual:** Introduce the equation A*v = λ*v, where A is the transformation matrix, v is the eigenvector, and λ is the eigenvalue. * **Animation:** Visually represent the equation. Show A acting on v, resulting in a scaled version of v (λ*v). Use color-coding to link the variables in the equation to their visual representations. For example, A could be represented by a colored box, v by the vector itself, and λ by a scalar value displayed numerically. * **Narration:** "The amount by which the eigenvector is scaled is called the eigenvalue, represented by λ (lambda). This equation, A*v = λ*v, is the fundamental equation of eigenvectors and eigenvalues." **3. Visualizing Eigenvalues (1:30 - 2:30):** * **Visual:** Show several vectors on the grid. Apply a transformation. Some vectors should change direction significantly, while one or two should remain on their original lines (eigenvectors). * **Animation:** Highlight the eigenvectors. Display their corresponding eigenvalues (λ) as numerical values next to them. If λ is negative, show the eigenvector flipping direction. * **Narration:** "Eigenvalues can be positive, negative, or even zero. A positive eigenvalue means the eigenvector is scaled in the same direction. A negative eigenvalue means it's scaled and flipped. A zero eigenvalue means the eigenvector is squashed to the origin." * **Visual:** Show examples of each case (positive, negative, and zero eigenvalues) with clear visual representations. **4. Finding Eigenvectors (2:30 - 3:30):** * **Visual:** Start with the equation A*v = λ*v. Rearrange it to (A - λI)*v = 0, where I is the identity matrix. * **Animation:** Visually demonstrate the matrix subtraction (A - λI). Show the identity matrix I being scaled by λ and then subtracted from A. * **Narration:** "To find the eigenvectors, we need to solve this equation. We rearrange it to (A - λI)*v = 0. This means the determinant of (A - λI) must be zero." * **Visual:** Show the determinant of (A - λI) being calculated. Visually represent the determinant as the area scaling factor of the transformation represented by (A - λI). * **Animation:** Show how the determinant changes as λ varies. When the determinant is zero, highlight the corresponding value of λ. * **Narration:** "The values of λ that make the determinant zero are the eigenvalues. Once we have the eigenvalues, we can plug them back into the equation (A - λI)*v = 0 to find the corresponding eigenvectors." **5. Importance of Eigenvectors and Eigenvalues (3:30 - 4:30):** * **Visual:** Show a more complex transformation. Then, show the same transformation represented as a combination of scaling along the eigenvectors. * **Animation:** Decompose the transformation into its eigenvector components. Show how the transformation can be understood as scaling along the eigenvectors. * **Narration:** "Eigenvectors and eigenvalues allow us to understand complex transformations by breaking them down into simpler scaling operations along specific directions. They provide a fundamental understanding of the transformation's behavior." * **Visual:** Briefly show examples of applications of eigenvectors and eigenvalues, such as: * **Principal Component Analysis (PCA):** Show data points clustered in an ellipse, and highlight the eigenvectors representing the principal components. * **Vibrational Modes:** Show a vibrating string or structure, and highlight the eigenvectors representing the different modes of vibration. * **Google's PageRank Algorithm:** Show a network of web pages and briefly mention how eigenvectors are used to determine the importance of each page. **6. Conclusion (4:30 - 5:00):** * **Visual:** Reiterate the equation A*v = λ*v. * **Animation:** Show the eigenvector and eigenvalue visually, emphasizing their relationship. * **Narration:** "Eigenvectors and eigenvalues are powerful tools for understanding linear transformations. They reveal the fundamental directions and scaling factors that govern the transformation's behavior. They are essential concepts in linear algebra and have wide-ranging applications in various fields." * **Visual:** End with a visually appealing animation of eigenvectors and eigenvalues, perhaps showing them rotating or interacting in a dynamic way. **Style and Pacing:** * **Visual Style:** Use a clean, minimalist style with clear color-coding, similar to 3Blue1Brown's animations. Use smooth transitions and animations to maintain viewer engagement. * **Pacing:** Maintain a steady pace, allowing sufficient time for viewers to grasp each concept. Use pauses and visual cues to emphasize key points. * **Narration:** Use a clear, concise, and engaging voiceover. Explain concepts in a simple and intuitive way, avoiding overly technical jargon. * **Music:** Use background music that is subtle and supportive of the animation's message. **Technical Details:** * **Software:** Ideally, use a software package that allows for precise control over animation and mathematical visualization (e.g., Manim, Blender with Python scripting). * **Resolution:** 1920x1080 (Full HD) * **Frame Rate:** 30 fps **Key Considerations for the Animator:** * **Intuition over Rigor:** Focus on building intuition rather than providing rigorous mathematical proofs. * **Visual Clarity:** Prioritize visual clarity and avoid cluttering the screen with too much information. * **Storytelling:** Tell a compelling story that engages the viewer and makes the concepts memorable. This prompt provides a detailed outline for creating a 3Blue1Brown-style animation on eigenvectors and eigenvalues. You can adapt this prompt to other mathematical concepts as well. Remember to emphasize visual clarity, intuitive explanations, and a compelling narrative. Good luck!

AGI-MCP

An advanced MCP server implementing a cognitive architecture through the GOTCHA framework and ATLAS process for sophisticated task management and reasoning. It provides persistent SQLite memory, lifecycle hooks, and a subagent system to enable complex, agentic AI workflows.

XFOIL MCP Server

Enables aerodynamic analysis through XFOIL polar computations. Provides typed models and tools to run airfoil performance analyses from agents or automation workflows.

AbletonMCP

A server that connects Ableton Live to Claude AI through the Model Context Protocol, enabling AI-assisted music production and direct control of Ableton Live features.

Senzing MCP Server

Enables entity resolution capabilities through the Senzing SDK, allowing AI assistants to search entities, manage records, analyze relationships between entities, and perform bulk data imports with multithreading.

LinkedIn Intelligence MCP Server

Connects Claude Desktop to LinkedIn's data layer for AI-powered networking, enabling profile research, content creation and scheduling, engagement automation, analytics tracking, and messaging through natural language.

Project Memory

An intelligent personal CRM that processes WhatsApp conversations to build a searchable knowledge base about contacts using diarization, transcription, and PII sanitization. It exposes MCP tools for semantic search, contact summaries, and reminder management within Claude Desktop.

Brave Search

Web and local search using Brave's Search API

LiteFarm MCP Server

Connects Claude Desktop to a local LiteFarm installation, enabling farm management, task operations, crop browsing, and direct SQL database operations through natural language commands.

MCP Python Server

A Python-based implementation of the Model Context Protocol that enables communication between a model context management server and client through a request-response architecture.

WordPress MCP Server

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Spotify MCP Server

Enables interaction with Spotify's music catalog through natural language conversations. Search for tracks and artists, get recommendations, explore playlists, and browse artist discographies using the Spotify Web API.

Wikidata MCP Server

Connects LLMs to Wikidata's structured knowledge base using a hybrid architecture that optimizes for both fast entity searches and complex relational queries. It provides tools for entity and property retrieval, metadata lookups, and direct SPARQL execution to ground AI responses in verified data.

MCP Gateway

Reduces LLM context window overhead by proxying multiple MCP servers through a few efficient dispatch tools instead of registering hundreds of individual tool schemas. It supports multi-account routing and tool discovery for both CLI-based and persistent MCP server configurations.

Datadog MCP Server

Enables comprehensive Datadog monitoring capabilities including CI/CD pipeline management, service logs analysis, metrics querying, monitor and SLO management, service definitions retrieval, and team management through Claude and other MCP clients.

librarian

An MCP server that enables LLMs to search, summarize, and retrieve detailed information from Wikipedia across multiple languages. It supports automated fact-checking by allowing models to proactively verify factual claims using Wikipedia's database.

mcp-scholar

"mcp\_scholar" es una herramienta basada en Python para buscar y analizar artículos de Google Scholar, que admite funciones como búsquedas basadas en palabras clave e integración con clientes MCP y Cherry Studio. Proporciona funcionalidades como obtener los artículos más citados de los perfiles de Scholar y resumir las principales investigaciones.

CodeGraphContext

Indexes local Python code into a Neo4j graph database to provide AI assistants with deep code understanding and relationship analysis. Enables querying code structure, dependencies, and impact analysis through natural language interactions.

Permission Marketing MCP

Operationalizes Seth Godin's Permission Marketing framework to manage user trust and delegation in AI agent systems through a structured five-level permission ladder. It provides tools for requesting, auditing, and revoking permissions to enable autonomous agent actions within user-defined guardrails.

connectwise-sell-mcp

MCP server wrapping the ConnectWise Sell REST API, providing tools to manage quotes, line items, tabs, customers, terms, templates, recurring revenue, and tax codes.