An MCP server that gives LLM agents structured, safe, and traceable access to engineering project documentation stored in Markdown/Git repositories, enabling management of requirements, decisions, tests, tasks, and impact analysis.
An MCP server that provides a self-improving knowledge graph with per-triple provenance and deterministic reasoning, enabling auditable, reproducible, and contradiction-aware answers for AI agents.
An MCP server that provides a permission-aware context layer over team tools, enabling AI agents to recall, search, and write to a shared memory graph with ACL-bound retrieval.
An MCP server that implements the Contract-First Agentic Workflow (CFAW) methodology for AI-assisted software engineering, using a Mixture-of-Agents architecture with six tools to enforce contract-first development and maintain architectural integrity across coding sessions.
A governed MCP server that enforces a trust layer between AI agents and databases, requiring sign-off on joins and metrics and producing auditable receipts for every query.
