Sequa MCP Server

Sequa MCP – Bridging AI Assistants with Context‑Rich Codebases

Sequa MCP solves the perennial problem of context starvation for AI assistants. When a language model is asked to refactor, document, or debug code, it typically has no direct access to the current state of a project spread across multiple repositories. Sequa fills this gap by acting as a Contextual Knowledge Engine that aggregates code, documentation, and repository metadata in real time. By exposing this data through the Model Context Protocol (MCP), Sequa allows any MCP‑capable client—such as Cursor, Claude Desktop, VS Code, or Highlight AI—to inject deep, up‑to‑date project knowledge into the assistant’s prompt without manual configuration.

At its core, Sequa MCP is a thin proxy that translates the standard STDIO/command‑based transport used by many IDEs into Sequa’s native, streamable HTTP MCP endpoint. A single command line invocation () launches a lightweight server that listens for MCP requests, authenticates against the user’s Sequa project, and streams contextual information back to the assistant. This design eliminates the need for developers to manage separate network listeners or complex authentication flows; they simply point their editor’s MCP configuration to the same URL that Sequa uses for its own web interface.

Key capabilities include:

• Cross‑repo awareness – Sequa can pull context from multiple repositories linked to a single project, giving assistants a holistic view of the codebase.
• Live streaming – Context is delivered as an incremental stream, allowing assistants to start generating responses while data is still arriving.
• Project‑level rules and best practices – Developers can define coding standards, style guides, or compliance checks that the assistant automatically applies to generated code.
• Extensibility via MCP – Because Sequa follows the standard MCP schema, any future enhancements (e.g., custom tool integrations or prompt templates) can be added without breaking existing clients.

Typical use cases abound in modern development workflows. A developer working on a large microservices architecture can ask the assistant to refactor a shared utility function, and Sequa will provide the latest implementation from all relevant repositories, ensuring consistency. A team can enforce architectural constraints by embedding a “no‑global‑state” rule in Sequa’s context, so the assistant never suggests violating patterns. In CI/CD pipelines, an automated agent can generate documentation or unit tests on the fly, leveraging Sequa’s live repository data to keep artifacts current.

Integration is straightforward: most MCP‑enabled editors already expose a configuration file where the server URL or command can be specified. Sequa’s single‑command launch script works out of the box with these tools, requiring only a copy of the project’s MCP setup URL. Once configured, every interaction with the AI assistant automatically carries the enriched context, dramatically improving accuracy and reducing the need for manual prompts.

In summary, Sequa MCP turns AI assistants from generic chatbots into project‑aware collaborators. By providing real‑time, cross‑repo context through a standardized protocol, it empowers developers to write better code faster, maintain consistency across large codebases, and embed organizational best practices directly into the AI’s reasoning process.