相关论文: mcp-proto-okn: Natural-language access to open sci…

The Model Context Protocol (MCP) replaces static, developer-controlled API integrations with more dynamic, user-driven agent systems, which also introduces new security risks. As MCP adoption grows across community servers and major…

The Model Context Protocol (MCP) has emerged as a standard for connecting large language models (LLMs) with external tools. However, this MCP ecosystem introduces new security risks across hosts, servers, and registries. In this paper, we…

The Model Context Protocol (MCP), introduced by Anthropic, provides a standardized framework for artificial intelligence (AI) systems to interact with external data sources and tools in real-time. While MCP offers significant advantages for…

AI-assisted developer services are increasingly embedded in modern IDEs, yet enterprises must ensure these tools operate within existing identity, access control, and governance requirements. The Model Context Protocol (MCP) enables AI…

The Model Context Protocol (MCP) has emerged as a standard for connecting Large Language Models (LLMs) to external tools and data. However, MCP servers often expose privileged capabilities, such as file system access, network requests, and…

Retrieval over knowledge graphs is usually performed using dedicated, complex query languages like SPARQL. We propose a novel system, Ontology and Semantic Exploration Toolkit (OnSET) that allows non-expert users to easily build queries…

Colored Petri Nets (CPNs) are an established formalism for modeling processes where tokens carry data. Although tools like CPN Tools and CPN IDE excel at CPN-based simulation, they are often separate from modern data science ecosystems.…

With an increase in Geospatial Linked Open Data being adopted and published over the web, there is a need to develop intuitive interfaces and systems for seamless and efficient exploratory analysis of such rich heterogeneous multi-modal…

Classical CNN based object detection methods only extract the objects' image features, but do not consider the high-level relationship among objects in context. In this article, the graph convolutional networks (GCN) is integrated into the…

AI assistants can decompose multi-step workflows, but they do not natively speak industrial protocols such as Modbus, MQTT/Sparkplug B, or OPC UA, so this paper presents INDUSTRICONNECT, a prototype suite of Model Context Protocol (MCP)…

Large language models show great potential in unstructured data understanding, but still face significant challenges with graphs due to their structural hallucination. Existing approaches mainly either verbalize graphs into natural…

The relatively recent adoption of Knowledge Graphs as an enabling technology in multiple high-profile artificial intelligence and cognitive applications has led to growing interest in the Semantic Web technology stack. Many…

Recent advancements in graph-based analysis and solutions of instantly decodable network coding (IDNC) trigger the interest to extend them to more complicated opportunistic network coding (ONC) scenarios, with limited increase in…

The Model Context Protocol (MCP) has emerged as a standardized interface enabling seamless integration between Large Language Models (LLMs) and external data sources and tools. While MCP significantly reduces development complexity and…

The increased adoption of the Model Context Protocol (MCP) for AI Agents necessitates robust security for Enterprise integrations. This paper introduces the MCP Gateway to simplify self-hosted MCP server integration. The proposed…

In an IoT environment, which is characterized by a multitude of interconnected smart devices with sensing and computational capabilities, many applications are (i) content-based, that is, they are only interested in finding a given type of…

This paper introduces Agentic-AI Healthcare, a privacy-aware, multilingual, and explainable research prototype developed as a single-investigator project. The system leverages the emerging Model Context Protocol (MCP) to orchestrate…

Large language model powered autonomous agents demand robust, standardized protocols to integrate tools, share contextual data, and coordinate tasks across heterogeneous systems. Ad-hoc integrations are difficult to scale, secure, and…

Agentic AI systems built around large language models (LLMs) are moving away from closed, single-model frameworks and toward open ecosystems that connect a variety of agents, external tools, and resources. The Model Context Protocol (MCP)…

This paper presents LAGOON -- an open source platform for understanding the complex ecosystems of Open Source Software (OSS) communities. The platform currently utilizes spatiotemporal graphs to store and investigate the artifacts produced…