Central Memory MCP Server

Central Memory is a Model Context Protocol (MCP) memory server that gives AI assistants persistent, graph‑based knowledge storage directly within the VS Code ecosystem. By running on Azure Functions and backed by Azure Table Storage, it delivers a lightweight yet scalable solution that isolates each workspace’s data, ensuring privacy and modularity. This server addresses the common pain point of “context loss” in AI‑driven development: every time a conversation starts, the assistant has no recollection of prior interactions or domain knowledge unless that information is explicitly fed back. Central Memory solves this by allowing developers to create, query, and evolve a knowledge graph that persists across sessions, tooling, and even different assistants.

The server exposes a rich set of MCP tools that map closely to typical graph operations. Users can read the entire graph, search for entities or relations, and perform CRUD operations on nodes and edges. What sets Central Memory apart is its auto‑creation logic: when an observation or relation references an entity that does not yet exist, the server automatically creates a placeholder node. This reduces friction for developers who may forget to pre‑define every entity, and it keeps the graph coherent. The tools also provide detailed error messages with actionable examples when validation fails, improving developer experience and reducing trial‑and‑error cycles.

Key capabilities include:

• Workspace isolation – each project has its own storage container, preventing cross‑project contamination.
• Temporal event tracking – the tool lets developers query time‑based activity, useful for audit trails or progress monitoring.
• Duplicate management – and help maintain data quality in growing graphs.
• Batch operations – enables atomic execution of multiple graph mutations, improving performance for bulk imports.
• Statistical insights – and provide metrics on graph size, entity density, and user contributions.

In practice, Central Memory shines in scenarios such as:

• Project onboarding – a new developer can query the existing knowledge graph to understand team roles, project dependencies, and technical debt.
• Feature documentation – AI assistants can automatically enrich code comments with observations about design decisions, linking them to relevant modules and stakeholders.
• Continuous integration pipelines – CI tools can record build events or test results as observations, creating a searchable history of quality metrics.
• Knowledge management – teams can maintain an evolving ontology of product features, customer personas, or compliance requirements that the assistant can reference in real time.

Integrating Central Memory into an AI workflow is straightforward: once the server is running, developers invoke MCP tools via VS Code Copilot chat or any compatible client. The object‑parameter style (e.g., ) provides type safety and clarity. A typical workflow begins with to visualize the current state, followed by targeted searches and incremental updates. Because each operation is idempotent and validated, developers can experiment freely without fear of corrupting the knowledge base.

Overall, Central Memory offers a robust, developer‑friendly bridge between AI assistants and persistent, graph‑structured knowledge. Its auto‑creation features, temporal insights, and batch capabilities give teams a powerful toolset for maintaining contextual continuity, accelerating onboarding, and ensuring that AI assistance remains grounded in real project data.