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This page describes the core design of the Neurodivergent Memory MCP Server: the district model, canonical tag schema, and retrieval/graph operations.
Memories are partitioned into five cognitive districts that provide intentional context boundaries while still permitting cross-district graph connections.
| District | Purpose |
|---|---|
logical_analysis |
Analytical reasoning, deductions, structured thinking |
emotional_processing |
Emotional context, tone, affective state |
practical_execution |
Task tracking, action items, procedural steps |
vigilant_monitoring |
Risk awareness, anomalies, watchpoints |
creative_synthesis |
Ideation, patterns, generative associations |
The district model is inspired by how neurodivergent cognition naturally partitions attention across parallel, non-linear contexts. Rather than flattening all memories into a single namespace, districts let agents — and humans — scope recall intentionally.
Cross-district connections are fully supported via the graph layer, allowing a memory in creative_synthesis to explicitly relate to one in logical_analysis.
All memories use structured tags in four namespaces to improve consistency, discoverability, and retrieval quality.
| Namespace | Purpose | Examples |
|---|---|---|
topic:X |
Subject matter of the memory |
topic:release, topic:architecture
|
scope:X |
Breadth or boundary of the memory |
scope:project, scope:session
|
kind:X |
Type of cognitive entry |
kind:decision, kind:observation, kind:plan
|
layer:X |
Abstraction level |
layer:system, layer:agent, layer:user
|
This schema applies to both human-authored and agent-authored entries, ensuring retrieval quality is consistent regardless of authorship.
The system uses BM25 (Best Match 25) for ranked lexical search over memory content. BM25 weighs term frequency and inverse document frequency, making it well-suited for short, dense memory entries.
Memories can be connected via explicit typed relationships. Graph traversal allows the server to:
- surface associatively linked memories during retrieval,
- trace chains of related context across districts,
- and support future goal-aware and orchestration-aware retrieval strategies.
The server exposes state and health inspection through memory statistics tools, allowing agents to introspect the current memory store without performing a full retrieval.
The architecture is explicitly built for non-linear cognition. Rather than assuming a single flat thread of recall, it:
- Partitions context into districts with intentional boundaries.
- Applies structured metadata so any entry is independently navigable.
- Uses graph relationships to honor associative, non-sequential memory patterns.
- Ranks retrieval by relevance rather than insertion order.
This makes the system natural for neurodivergent users and well-suited for multi-agent workflows where different agents operate in different cognitive modes simultaneously.
See also: Release-Notes · Roadmap · White-Paper