Model Context Protocol (MCP) compliant memory & knowledge graph server implemented in .NET 10 (Azure Functions isolated worker). Provides durable project memory (entities, relations, observations) for AI assistants with workspace isolation and simple HTTP tool endpoints.
Current function set (alpha stage):
- read_graph - returns all entities for a workspace (relations currently queried separately and joined)
- upsert_entity - create or update entity (preserves existing Id if name exists)
- upsert_relation - create or update relation between two entities (requires GUIDs or resolvable names)
- get_entity_relations - list relations originating from a specific entity
- Health & Ready endpoints (/api/health, /api/ready)
Planned (not yet implemented): search_entities, search_relations, stats, temporal, batch operations, merge/detect duplicates.
- Entities table: PartitionKey = WorkspaceName, RowKey = Guid (Id)
- Relations table: PartitionKey = WorkspaceName, RowKey = Guid (Id)
- Workspaces table (future expansion)
Observations stored as a single delimited string ("||") internally; split into List at read time.
dotnet restore
dotnet build
func start --port 7071
curl http://localhost:7071/api/healthTo read the graph, invoke the read_graph MCP tool from the client and supply the workspaceName parameter.
flowchart TD
A[CentralMemoryMcp.Functions]
A --> P[Program.cs]
A --> SR[ServiceRegistration.cs]
A --> F[Functions]
F --> GF[GraphFunctions.cs]
F --> HF[HealthFunctions.cs]
A --> S[Services]
S --> KGS[KnowledgeGraphService.cs]
A --> ST[Storage]
ST --> TSS[TableStorageService.cs]
A --> M[Models]
M --> GM[GraphModels.cs]
A --> CFG[appsettings.json]
A --> HOST[host.json]
- Use workspaceName consistently; workspaceId in docs replaced.
- Upsert preserves entity identity by lookup on (WorkspaceName + Name).
- Relation upsert requires entity GUIDs or resolves names; fails if names missing.
Add Application Insights connection to capture request latency & storage dependency tracking (future enhancement).
Add search, pagination, stats aggregation, batch operations, duplicate detection, merge strategy, semantic vector layer.
MIT.