SDOF MCP Server

Overview of the SDOF MCP Server

The Structured Decision Optimization Framework (SDOF) MCP server is a next‑generation knowledge‑management platform built around the Model Context Protocol. It addresses a common pain point for AI developers: maintaining coherent, searchable, and reusable context across long‑running conversations or multi‑step workflows. By persisting structured content in a vector‑indexed database and exposing it through MCP tools, the server lets AI assistants like Claude remember past decisions, evaluate alternatives, and generate code or documentation that builds on earlier insights.

At its core, SDOF implements a five‑phase optimization workflow—Exploration, Analysis, Implementation, Evaluation, and Integration. Each phase corresponds to a distinct content type (e.g., , , ) and is annotated with metadata such as phase number, tags, and caching hints. This structure enables developers to trace the evolution of a project from brainstorming to deployment, ensuring that every change is documented and retrievable. The tool captures Markdown‑formatted content along with rich metadata, making it trivial to store a design decision or an evaluation report and later retrieve it by semantic search.

The server’s value lies in its advanced knowledge‑management features. OpenAI embeddings provide deep semantic search, while MongoDB or SQLite backends offer scalable vector indexing and persistence. Prompt caching reduces token usage by reusing frequently requested content, and schema validation guarantees that stored records adhere to expected formats. Developers can interact with the system either via MCP tools or a standard HTTP API, giving flexibility for integration into existing pipelines or custom front‑ends.

Real‑world use cases abound: a product team can store architecture decisions, a data scientist can capture model evaluation metrics, and an engineer can retrieve code snippets that were generated earlier in a conversation. In all scenarios, the AI assistant can query the knowledge base to avoid redundant work, maintain consistency across documents, and provide contextually relevant answers. The server’s ability to tie content to specific phases also facilitates audit trails and knowledge transfer, which are critical in regulated or collaborative environments.

Overall, the SDOF MCP server offers a structured, persistent, and semantically rich knowledge layer that enhances AI workflows. By bridging the gap between transient LLM responses and long‑term project artifacts, it empowers developers to build more reliable, transparent, and maintainable AI‑driven systems.