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ODW #3: Boosting Development Efficiency by Safely Utilizing MCP Servers (opens in new tab)

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LY Corporation is expanding AI use across its engineering organization through MCP servers, which connect AI assistants with internal and external tools through a common protocol. The company combines this flexibility with allowlists, automated security checks, and internal standards to reduce risk. Its Orchestration Development Workshop demonstrates practical applications such as Jira ticket automation and multi-agent code reviews, while emphasizing shared learning and experimentation as AI practices evolve.

MCP Servers and Their Benefits

  • MCP servers act as translators between AI assistants and external systems.
  • Before MCP, each assistant required a separate integration for every tool.
  • With MCP, a tool can implement one standardized interface and work with multiple compatible assistants.
  • This improves interoperability, scalability, and the ability to combine different AI tools.

Security Risks and LY Corporation’s Controls

  • A 2025 Astrix Security report found that:
    • More than 5,200 public MCP servers were analyzed.
    • 53% relied on long-lived static API keys or personal access tokens.
    • Only 8.5% used newer authentication methods such as OAuth.
  • LY Corporation manages externally developed MCP servers through:
    • An allowlist permitting only approved servers.
    • Automated security verification based on internal standards.
  • Internal MCP servers for groupware and business systems are built to meet the company’s security requirements.
  • Centralized infrastructure lets teams focus on applying AI rather than independently rebuilding integrations and controls.

Workshop Applications

The Orchestration Development Workshop taught participants how to understand, configure, and safely apply MCP servers with AI assistants.

  • Topics included MCP fundamentals, security risks, internal policies, development rules, and configuration in Claude and Cline.
  • The internal plugin marketplace was introduced as a way to distribute MCP configurations.
  • Participants practiced using Claude Code with the internal groupware MCP server to:
    • Generate a Jira ticket title and summary.
    • Create the ticket automatically.
  • The exercise showed how AI can remove repetitive administrative work and free time for higher-value tasks.

Multi-Agent Code Review Demonstration

  • A demonstration combined Claude Code, Codex CLI, Context7 MCP, and Codex MCP.
  • A Sonnet-based agent first analyzed a pull request, including:
    • Technical stack and relevant documentation.
    • Code changes and repository context.
    • Security, performance, and code-quality concerns.
  • GPT-5 then validated the initial review, identifying missed issues and checking the prioritization of findings.
  • Using different models provided more varied and potentially objective perspectives on the same code.

Results and Organizational Learning

  • Around 1,600 people attended the workshop in real time.
  • 31.5% had already applied related techniques before the event.
  • Another 55.7% planned to try them soon.
  • LY also created “Help LY MCP,” a GPTs-based tool that explains internal MCP rules and helps teams assess whether proposed uses are suitable, including for global subsidiaries.
  • The workshop’s broader purpose was to create a shared understanding of:
    • What AI and MCP can currently do.
    • What risks and pitfalls exist.
    • How to use the technology meaningfully.

Continuing to Experiment

The article concludes that rapidly changing AI technology makes shared experimentation more valuable than simply announcing new tools. MCP may eventually be surpassed by other approaches, such as skills, so teams should continually reassess the best solution. LY recommends creating a culture where employees can safely try small ideas, learn together, and adapt as new practices emerge.