Model Context Protocol (MCP) at First Glance: Studying the Security and Maintainability of MCP Servers (2506.13538v4)

Abstract: Although Foundation Models (FMs), such as GPT-4, are increasingly used in domains like finance and software engineering, reliance on textual interfaces limits these models' real-world interaction. To address this, FM providers introduced tool calling-triggering a proliferation of frameworks with distinct tool interfaces. In late 2024, Anthropic introduced the Model Context Protocol (MCP) to standardize this tool ecosystem, which has become the de facto standard with over eight million weekly SDK downloads. Despite its adoption, MCP's AI-driven, non-deterministic control flow introduces new risks to sustainability, security, and maintainability, warranting closer examination. Towards this end, we present the first large-scale empirical study of MCP servers. Using state-of-the-art health metrics and a hybrid analysis pipeline, combining a general-purpose static analysis tool with an MCP-specific scanner, we evaluate 1,899 open-source MCP servers to assess their health, security, and maintainability. Despite MCP servers demonstrating strong health metrics, we identify eight distinct vulnerabilities - only three overlapping with traditional software vulnerabilities. Additionally, 7.2% of servers contain general vulnerabilities and 5.5% exhibit MCP-specific tool poisoning. Regarding maintainability, while 66% exhibit code smells, 14.4% contain nine bug patterns overlapping with traditional open-source software projects. These findings highlight the need for MCP-specific vulnerability detection techniques while reaffirming the value of traditional analysis and refactoring practices.

• The paper reveals that 7.2% of MCP servers face traditional security flaws while 5.5% exhibit unique vulnerabilities like tool poisoning.
• It employs a hybrid analysis with SonarQube and mcp-scan on 1,899 servers to assess code smells, bugs, and development activity.
• The study calls for tailored security taxonomies and maintenance strategies to ensure the robust sustainability of MCP ecosystems.

Analyzing the Security and Maintainability of MCP Servers

The paper "Model Context Protocol (MCP) at First Glance: Studying the Security and Maintainability of MCP Servers" presents an empirical paper analyzing the security vulnerabilities, maintainability, and sustainability of MCP servers. Given the rising prominence of Foundation Models (FMs) like GPT-4 and their integration into various domains via AI-enabled applications, the authors' inquiry resonates with the growing need to address security and maintainability concerns within this space.

The paper outlines the adoption of the Model Context Protocol (MCP), introduced in late 2024, aimed at enhancing interoperability across AI tools by standardizing tool interfaces via a client-server architecture. With more than eight million weekly SDK downloads, MCP's significant uptake indicates its integral role in the expanding AI tool ecosystem. However, the inherent non-deterministic control flow of AI-driven MCP servers raises important questions about security risks and long-term maintainability.

To assess these concerns, the authors conduct a large-scale empirical paper on 1,899 open-source MCP servers employing a hybrid analysis methodology. The research utilizes a combination of general-purpose static analysis tools like SonarQube and an emerging MCP-specific scanner named mcp-scan, examining the health, security, and maintainability across these servers. The paper's results are structured around three research questions: the sustainability of MCP servers, their security vulnerabilities, and their maintainability issues, namely code smells and bugs.

The development and community metrics of MCP servers suggest promising sustainability when compared to general open-source software benchmarks. For example, MCP servers have a distinctly higher median commit frequency and CI adoption rate, indicating robust development activity and community engagement despite their nascent state. Mined MCP servers exhibit greater development activity and project size, compared to official and community MCP servers, implying notable early adopter momentum.

Interestingly, in terms of security, the work highlights the prevalence of vulnerabilities specific to MCP servers, with 7.2% demonstrating traditional vulnerabilities such as credential exposure and improper resource management. MCP-specific issues like tool poisoning affected 5.5% of servers. Notably, these vulnerabilities diverge significantly from those prevalent in ecosystems like PyPI or NPM, suggesting the need for tailored detection methodologies capable of addressing MCP-specific threats. Nonetheless, credential exposures in MCP servers can lead to severe consequences, such as unauthorized access and financial loss.

Regarding maintainability, 66% of MCP servers contain critical or blocker-level code smells, and 14.4% exhibit similar levels of bugs. High cognitive complexity is the most prevalent code smell, notable for its potential impact on understandability and debugging time. Interestingly, despite the different focus of MCP servers, their maintainability concerns align substantially with those documented in traditional software and ML projects, implying that existing refactoring and debugging strategies could be adapted for use in MCP.

The implications for the MCP ecosystem are multidimensional. For researchers, the paper encourages the expansion of security taxonomies to encompass MCP-specific threats and the development of custom vulnerability analysis tools. Practitioners, particularly those engaged in MCP development, are advised to proactively adopt security measures and leverage established ML and LLM-based techniques to detect and address maintainability issues. Ecosystem maintainers, such as MCP registries, are prompted to implement governance procedures that enforce automated scanning and revocation practices, mirroring those in mature software distribution platforms.

Overall, the paper provides a well-rounded perspective on the MCP ecosystem, striking a balance between positive sustainability signals and pressing security and maintainability challenges. As MCP continues to shape the interfacing of AI applications with real-world data, addressing these concerns will be pivotal to maintaining the integrity and longevity of the ecosystem.