Latest news with #MCP
Eyewitness News
12 hours ago
- Politics
- Eyewitness News
Govt depts now allowed to enlist IT services outside of SITA
Published on June 2, 2025 at 1:14 PM by Edgar Naitha The Malawi Congress Party (MCP) has dismissed allegations that it is planning to form an alliance with the newly registered political party, Odya Zake Alibe Mlandu. MCP's Second Deputy Publicity Secretary, Ken Msonda, described such claims as politically immature, especially with just three months remaining before the general elections. Msonda stated that the party is currently focused on national development and has no interest in alliance discussions. He expressed confidence that the MCP will win the upcoming elections independently. His remarks follow growing speculation among some Malawians who believe Odya Zake Alibe Mlandu is the most likely party to align with the MCP ahead of the 2025 polls. Source: Capital FM Subscribe to our Youtube Channel:
Geeky Gadgets
15 hours ago
- Business
- Geeky Gadgets
Why MCP is the Key to Unlocking AI's Full Potential in 2025
What if artificial intelligence could not only understand your needs but also act on them autonomously, seamlessly integrating with the tools and systems you rely on every day? This isn't a distant dream—it's the promise of the Model Context Protocol (MCP). While many AI systems today excel at generating insights or processing data, they often fall short when it comes to taking meaningful, real-world actions. MCP changes the game by providing a structured framework that connects AI models to external tools, APIs, and data sources, allowing them to operate in dynamic environments. In a world where businesses demand more than just passive AI, MCP emerges as a fantastic solution, bridging the gap between potential and practical application. In this exploration, Tim Berglund explains why MCP is more than just another AI framework—it's a cornerstone for agentic AI systems that can act independently and deliver tangible results. You'll learn how its modular and pluggable architecture enables organizations to build scalable, adaptable AI solutions that evolve alongside their needs. From scheduling meetings autonomously to integrating with complex enterprise systems, MCP unlocks new possibilities for intelligent applications. But what makes it truly innovative is its ability to shift AI from being a passive assistant to an active problem solver. As we delve into its architecture, features, and real-world applications, you'll discover why MCP isn't just a big deal—it's a glimpse into the future of AI-driven innovation. Overview of Model Context Protocol Agentic AI: From Passive Systems to Autonomous Action Agentic AI systems are designed to go beyond passive responses, allowing them to take meaningful actions. For example, instead of merely suggesting a meeting time, an agentic AI system can autonomously schedule the meeting by interacting with a calendar API. This ability to act independently is critical for real-world applications where AI must deliver tangible results. Despite their capabilities, foundational AI models are inherently limited. They excel at generating text or processing data but lack the ability to dynamically access external tools or data sources. This limitation confines them to predefined contexts, restricting their functionality. MCP addresses this challenge by providing a structured framework that connects AI systems to external resources such as APIs, databases, files, and event streams. By doing so, MCP enables AI to operate in dynamic environments and deliver actionable outcomes. Understanding the MCP Architecture At the core of MCP lies a client-server architecture that assists efficient communication between AI systems and external tools. This architecture is built around two primary components: Host Application: The client-side interface that interacts with the MCP server. It uses the MCP client library to bridge the gap between the user and the AI system, making sure smooth communication. The client-side interface that interacts with the MCP server. It uses the MCP client library to bridge the gap between the user and the AI system, making sure smooth communication. MCP Server: The server-side component that provides access to external tools and resources. These capabilities are described through RESTful APIs, allowing the host application to query and use them effectively. Communication between the host application and the MCP server is achieved using JSON RPC over HTTP or Server-Sent Events (SSE). This ensures real-time, efficient interactions, which are essential for applications requiring immediate responses. By employing this architecture, MCP creates a robust framework for integrating AI systems with external tools. How the Model Context Protocol (MCP) Powers Agentic AI Solutions Watch this video on YouTube. Take a look at other insightful guides from our broad collection that might capture your interest in Agentic AI. Real-World Applications of MCP MCP's capabilities are best understood through practical scenarios. Consider a situation where an AI system is tasked with scheduling a meeting. Here's how MCP assists this process: The user's prompt triggers the host application to query the MCP server for relevant tools, such as a calendar API. The MCP server responds with descriptions of available tools and their functionalities. The host application interprets the AI model's analysis of these tools and refines its actions accordingly. The AI system autonomously uses the selected tool to schedule the meeting, completing the task efficiently and effectively. This workflow highlights MCP's ability to dynamically integrate AI systems with external resources, allowing them to perform complex tasks autonomously. By bridging the gap between AI models and real-world functionality, MCP unlocks new possibilities for intelligent applications. Key Features That Define MCP MCP's design incorporates several core features that make it particularly suited for enterprise applications: Pluggability: Tools and resources can be added, removed, or replaced without altering the core application. This ensures that systems remain flexible and adaptable to changing requirements. Tools and resources can be added, removed, or replaced without altering the core application. This ensures that systems remain flexible and adaptable to changing requirements. Discoverability: Host applications can query MCP servers to identify available tools and their capabilities. This allows AI systems to access the most relevant resources for any given task, enhancing their efficiency and effectiveness. Host applications can query MCP servers to identify available tools and their capabilities. This allows AI systems to access the most relevant resources for any given task, enhancing their efficiency and effectiveness. Composability: MCP servers can act as clients to other servers, allowing layered integrations. For instance, an MCP server could connect to Kafka topics to process real-time event streams, creating a seamless flow of information. These features make MCP a robust and adaptable framework for building AI systems that can evolve alongside organizational needs. By prioritizing flexibility and scalability, MCP ensures that AI systems remain relevant in a rapidly changing technological landscape. Scalability and Modular Development with MCP MCP is designed with scalability at its core, making it ideal for enterprise-level applications. Its modular architecture minimizes the need for hardcoding, allowing developers to create systems that can be easily updated or expanded. By using standardized communication protocols like JSON RPC and RESTful APIs, MCP ensures interoperability across diverse tools and platforms. For example, an enterprise could use MCP to integrate an AI-driven customer support system with multiple backend services, such as a CRM database, a ticketing system, and a real-time chat platform. Thanks to MCP's modular design, these integrations can be updated or replaced without disrupting the overall system. This adaptability ensures that the system remains functional and efficient as organizational needs evolve. The Role of MCP in Shaping AI Development The Model Context Protocol represents a pivotal advancement in the evolution of agentic AI systems. By allowing seamless integration with external tools and resources, MCP allows AI applications to perform complex, real-world tasks with precision and efficiency. Its modular, pluggable, and composable architecture makes it particularly well-suited for enterprise use cases, offering the scalability and adaptability required in today's fast-paced technological environment. For organizations aiming to harness the full potential of AI, MCP provides a powerful framework for building the next generation of intelligent applications. By bridging the gap between foundational AI models and real-world functionality, MCP positions itself as a cornerstone of future AI development, driving innovation and allowing AI to deliver meaningful, actionable outcomes. Media Credit: Confluent Developer Filed Under: AI, Top News Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
Yahoo
3 days ago
- Business
- Yahoo
Chat with Your Data: MindsDB Launches Open-Source AI Interface for Databases and Documents
MindsDB's new AI interface enables natural language conversations with enterprise datasets—unifying structured databases and unstructured knowledge with intelligent agentic orchestration SAN FRANCISCO, May 30, 2025 /PRNewswire/ -- MindsDB, the open-source enterprise AI platform, today announced the release of their AI chat interface in MindsDB Open Source. This platform enables users to interact with their connected databases and knowledge bases using natural language—merging semantic understanding and SQL querying in a single unified experience. Drawing inspiration from MindsDB's enterprise product line, the chat interface brings the advanced conversational capabilities of intelligent agents directly into the open source offering, allowing developers, data scientists, and business users alike to "talk to their data" with no-code simplicity. Solving the "Two Data Languages" ProblemFor decades, enterprises have faced a dual-language challenge in data access: SQL is powerful for querying structured databases but requires technical fluency and schema knowledge. Semantic search tools handle unstructured content but often operate in isolation. MindsDB eliminates this divide by using a conversational interface powered by its AI Agent technology—automatically interpreting user queries and orchestrating the right mix of SQL and semantic operations behind the scenes. For example, a user can ask: "What are the common themes in support tickets about feature X, and how does that correlate with user engagement metrics?" MindsDB intelligently splits this into: A semantic query to extract themes from support tickets. A parametric SQL query to retrieve structured usage data. A unified response, delivered conversationally in the UI. Breakthrough Architecture: Built for AI Agents & Human CollaborationMindsDB is underpinned by an innovative architecture tailored for AI-native systems: Model Context Protocol (MCP): Powers standardized access to data via tools exposed by the MindsDB's Federated Query Engine, enabling seamless integration with AI agents and platforms. Agent-to-Agent (A2A) Communication: Allows the Chat Agent to coordinate with specialized SQL and semantic Agents—laying the groundwork for scalable multi-agent AI systems. Knowledge Bases: Combine vector search, embedding models, and optional reranking into a coherent semantic layer, accessible via chat and programmatic APIs. Text2SQL Translation: Automatically generates accurate SQL queries from natural language inputs for relational, AI, and federated databases. A diagram in the launch blog visualizes this multi-layered architecture—from chat input through intelligent routing and query generation to synthesized output. Key Benefits and Use CasesRespond unlocks transformative capabilities for enterprise users: Democratized Access: Enables anyone in the organization to explore enterprise data and knowledge using natural language—no data analytics expertise required. Unified Insights: Combines structured and unstructured sources into a single, coherent answer. Accelerated Development: Developers can rapidly prototype AI features and agentic workflows with reusable patterns. Open Ecosystem Integration: Fully accessible via MCP (with A2A integration coming soon), enabling third-party agents and tools to interface with MindsDB as an intelligent backend. Availability and Getting StartedThe new Chat UI is now available in beta within the latest release of MindsDB Open Source. Detailed setup instructions are available in the MindsDB documentation. About MindsDBMindsDB enables humans, AI, agents, and applications to get highly accurate answers across disparate data sources and types. Unlocking AI Search and Analytics for enterprises, MindsDB unifies petabyte-scale structured and unstructured data across diverse data sources and applications. Powered by an industry-first cognitive engine that can operate anywhere (on-prem, VPC, serverless), it empowers both humans and AI with highly informed decision-making capabilities. Media ContactZubin Tavariamedia@ View original content: SOURCE MindsDB Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Yahoo
3 days ago
- Politics
- Yahoo
N.L. privacy commissioner investigating government response to school cyberattack
The province's information and privacy commissioner is investigating a security breach that saw hackers steal the private information of nearly 300,000 current and former students and teachers in Newfoundland and Labrador. In a news release sent Friday afternoon, Privacy Commissioner Kerry Hatfield said part of that investigation will look at whether the Education Department has taken enough action in the wake of the PowerSchool attack to make sure it doesn't happen again. "Before launching this investigation I felt it was appropriate to give the department sufficient time to assess the impact of the breach, notify those who were impacted, and take steps to adjust its policies and practices," she said in the release. "It has now had ample opportunity to do so." The late-December cyberattack struck PowerSchool, the data management software used by the English, French and Indigenous school systems — along with other school districts across North America. According to the Education Department, on Dec. 28 hackers stole the information of approximately 271,000 students and 14,400 teachers across Newfoundland and Labrador's English, French, and Indigenous school systems. The stolen data includes contact information, date of birth, MCP numbers, medical alert information, custodial alert information, some social insurance numbers and other related information. Some of that data dates back to 1995. The department said about 75 per cent of the stolen student data belongs to people who are no longer in the K-12 system. The company offered two years of free identity and credit monitoring to any of the victims, and has since hired Experian and TransUnion to provide those services. "The purpose of my investigation is not only to assess whether the department has responded adequately to the breach, but also to ensure that measures taken by the department to prevent future occurrences of this nature are sufficient," said Hatfield. "People have a right to expect that when a public body collects their sensitive personal information that it will do so in accordance with the law." Download our free CBC News app to sign up for push alerts for CBC Newfoundland and Labrador. Click here to visit our landing page.
Techday NZ
4 days ago
- Business
- Techday NZ
Kurrent unveils open-source MCP Server for AI-driven databases
Kurrent has released its open-source MCP Server for KurrentDB, enabling developers to interact with data in the KurrentDB database using natural language and AI agents rather than traditional coding methods. The Kurrent MCP Server offers new functionalities, allowing developers not only to query data but also to create, test, and debug projections directly through conversational commands. This feature is not available in other MCP server implementations, establishing a novel approach to database interaction by integrating AI-driven workflows into the database layer. Central to this release is the introduction of a self-correcting engine, which assists in automatically identifying and fixing logic errors during the prototyping phase. This reduces the need for manual debugging loops, streamlining the development process significantly for users building or modifying projections. The software is fully open-source and released under the MIT license, with documentation and a development roadmap available on GitHub. This permits both enterprise users and open-source contributors to adopt, customise, and improve the KurrentDB MCP Server without licensing restrictions. Kurrent MCP Server supports natural language prompts for tasks such as reading streams, listing streams within the database, building and updating projections, writing events to streams, and retrieving projection status for debugging. These capabilities aim to make the visual and analytical exploration of data more accessible and conversational for users with varying levels of technical expertise. The MCP Server is compatible with a broad range of frontier AI models, such as Claude, GPT-4, and Gemini. It can be integrated with popular IDEs and agent frameworks, including Cursor and Windsurf. This compatibility enables developers to leverage their preferred tools while reducing friction points typically associated with traditional database interactions. Addressing the new approach, Kirk Dunn, CEO of Kurrent, said, "Our new MCP Server makes it possible to use the main features of the KurrentDB database, like reading and writing events to streams and using projections, in a way that's as simple as having a conversation. The system's ability to test and fix itself reduces the need for debugging and increases reliability. Copilots and AI assistants become productive database partners rather than just code generators, seamlessly interfacing with KurrentDB." The server's key functions are designed to reduce development times for database tasks, enabling a focus on higher-value project work. Eight core capabilities are available, including Read_stream, List_streams, Build_projection, Create_projection, Update_projection, Test_projection, Write_events_to_stream, and Get_projections_status. Each of these responds directly to natural language instructions provided by the developer or AI agent. Kurrent has highlighted opportunities for the open source community to participate in the MCP Server's ongoing development. Developers can contribute code, report or tackle issues, and suggest new features through the project's GitHub repository and discussion forums. Comprehensive educational resources and installation guides are intended to help developers quickly integrate the MCP Server with KurrentDB for various use cases. Lokhesh Ujhoodha, Lead Architect at Kurrent, commented, "Before, database interactions required developers to master complex query languages, understand intricate data structures, and spend significant time debugging projections and data flows. Now, everything agentic can interface with KurrentDB through this MCP Server. We're not just connecting to today's AI tools, but we're positioning for a future where AI agents autonomously manage data workflows, make analytical decisions and create business insights with minimal human intervention." Kurrent emphasises that its MCP Server aims to remove barriers historically associated with database development by supporting conversational, agent-driven workflows. This aligns with broader trends towards AI-native infrastructure in enterprise environments, where human and algorithmic agents increasingly collaborate to deliver data-driven business outcomes.
