neurodivergent-memory MCP Server

📽️ Click to preview

Project Preview This is a Model Context Protocol server for knowledge graphs designed around neurodivergent thinking patterns. This TypeScript-based MCP server implements a memory system inspired by neurodivergent cognitive styles. It organizes thoughts into five districts (knowledge domains), ranks search results using BM25 semantic ranking, and stores memories as a persistent knowledge graph with bidirectional connections. Design note: The district model is rooted in FractalSemantics (FractalStat) addressing, where every entity inherits ancestry from a single anchor point called LUCA (Last Universal Common Ancestor). These concepts are also used in Warbler-CDA and the seed. The five canonical districts are the five direct children of LUCA in the default schema. Custom districts in later milestones must declare a valid LUCA-derived address, making ancestry explicit and traceable rather than assumed.

Quick-start

Windows

# Download and install Chocolatey:
powershell -c "irm https://community.chocolatey.org/install.ps1|iex"

# Download and install Node.js:
choco install nodejs --version="24.14.1"

# Verify the Node.js version:
node -v # Should print a Node.js 24.x version.

# Verify npm version:
npm -v # Should print an npm 11.x version.

# Run the packaged neurodivergent-memory CLI without a global install
npx neurodivergent-memory@latest init-agent-kit

Linux/macOS

# Download and install nvm:
curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.4/install.sh | bash

# in lieu of restarting the shell
. "$HOME/.nvm/nvm.sh"

# Download and install Node.js:
nvm install 24

# Verify the Node.js version:
node -v # Should print a Node.js 24.x version.

# Verify npm version:
npm -v # Should print an npm 11.x version.

# Run the packaged neurodivergent-memory CLI without a global install
npx neurodivergent-memory@latest init-agent-kit

Model Flow

flowchart LR
  A[Client MCP Request] --> B[MCP Server Stdio Transport]
  B --> C{Request Type}
  C -->|Tools| D[Tool Handler]
  C -->|Resources| E[Resource Handler]
  C -->|Prompts| F[Prompt Handler]

  D --> G[NeurodivergentMemory Core]
  E --> G
  F --> G

  G --> H[Memory Graph Store]
  G --> I[BM25 Index]
  H --> J[Persisted JSON Snapshot]

  D --> K[MCP JSON Response]
  E --> K
  F --> K
  K --> A

Flow notes:

• Memory operations update both graph state and BM25 index.
• Persistence writes to the local snapshot file for restart continuity.
• All MCP responses return through stdio transport.

Features

Five Memory Districts

Memories are organized by cognitive domain:

• logical_analysis — Structured thinking, problem solving, and analytical processes
• emotional_processing — Feelings, emotional responses, and affective states
• practical_execution — Action-oriented thoughts, tasks, and implementation
• vigilant_monitoring — Awareness, safety concerns, and protective thinking
• creative_synthesis — Novel connections, creative insights, and innovative thinking

Resources

• Explore memory districts and individual memories via memory:// URIs
• Each memory includes content, tags, emotional metadata, and connection information
• Access memories as JSON resources with full metadata

Tools

• store_memory — Create new memory nodes with optional emotional valence and intensity
• retrieve_memory — Fetch a specific memory by ID
• update_memory — Modify content, tags, district, emotional_valence, intensity, or project attribution
• delete_memory — Remove a memory and all its connections
• connect_memories — Create bidirectional edges between memory nodes
• search_memories — BM25-ranked semantic search with optional goal context, recency bias, and filters (district, project_id, tags, epistemic status, emotional valence, intensity, min_score)
• traverse_from — Graph traversal up to N hops from a starting memory
• related_to — Find memories by graph proximity + BM25 semantic blend, with optional goal context and epistemic-status filters
• list_memories — Paginated listing with optional district/archetype/project_id/epistemic-status filters
• memory_stats — Aggregate statistics (totals, per-district/per-project counts, most-accessed, orphans) with optional project scope
• server_handshake — Return runtime server identity/version details for explicit client-side version confirmation
• storage_diagnostics — Show the resolved snapshot path, WAL path, and effective persistence source in one response
• import_memories — Bulk-import from inline JSON entries or a snapshot file_path, with dry_run, dedupe policies, and explicit snapshot migration flags
• prepare_memory_city_context — Tool mirror of explore_memory_city for clients that support tools but do not invoke MCP prompts
• prepare_synthesis_context — Tool mirror of synthesize_memories for prompt-limited clients
• prepare_packetized_synthesis_context — Tool mirror of synthesize_memory_packets for prompt-limited or attachment-constrained clients

Prompts

• explore_memory_city — Guided exploration of districts and memory organization
• synthesize_memories — Create new insights by connecting existing memories
• synthesize_memory_packets — Packetized synthesis prompt for attachment-constrained clients; emits one coverage manifest plus bounded memory slices that summarize the broader graph

Use synthesize_memories when the MCP client can comfortably consume many raw memory resources. Use synthesize_memory_packets when the caller path is attachment-constrained or when you need broader graph coverage in a small number of structured resources.

For maximum interoperability across MCP clients, the server exposes the same synthesis/exploration context in two forms:

• Prompts via prompts/list + prompts/get for clients that implement MCP prompt invocation.
• Tools via the prepare_*_context tools for clients that support MCP tools but ignore or under-support prompts.

Some clients, such as Cline, expose MCP prompts as namespaced slash commands in the form /mcp:<server-name>:<prompt-name> rather than /<prompt-name>.

Core Concepts

Memory Archetypes

Each memory is assigned an archetype tied to its district:

• scholar — logical_analysis
• merchant — practical_execution
• mystic — emotional_processing and creative_synthesis
• guard — vigilant_monitoring

Semantic Ranking

Search uses Okapi BM25 ranking (k1=1.5, b=0.75) without requiring embeddings or cloud calls. Results are normalized to 0–1 score range.

Emotional Metadata

Each memory can optionally carry:

• emotional_valence (-1 to 1) — Emotional charge or affective tone
• intensity (0–1) — Mental energy or importance weight

Epistemic Status

Memories can optionally carry epistemic_status to distinguish tentative planning from validated knowledge.

• draft — provisional or planning-oriented
• validated — confirmed and safe to treat as established
• outdated — superseded but retained for history

When store_memory or import_memories creates a new practical_execution memory without an explicit epistemic_status, the server defaults it to draft if the memory has a task tag. The canonical task tag is kind:task, and the server also accepts the compatibility synonyms type:task and bare task. This keeps planning notes from silently presenting as settled fact.

Project Attribution and Scoped Retrieval

Memories can optionally include a first-class project_id for attribution and scoped retrieval across multi-project graphs.

• project_id is optional on writes (store_memory, update_memory, import_memories).
• update_memory accepts project_id: null to clear existing project attribution.
• search_memories, list_memories, and memory_stats accept an optional project_id filter.
• search_memories, list_memories, and related_to accept optional epistemic_statuses filters so callers can avoid stale planning memories when appropriate.
• search_memories accepts optional context and recency_weight parameters. Context is blended into ranking as a lightweight BM25 boost; recency_weight must be between 0 and 1 and adds a recency boost without replacing semantic relevance.
• search_memories accepts min_intensity / max_intensity as the preferred intensity filter names. The legacy intensity_min / intensity_max aliases remain supported for compatibility.
• related_to accepts an optional context parameter to bias related-memory ranking toward the caller's current goal.
• Stats now include a perProject breakdown.
• Scoped memory_stats reports totalConnections only for edges where both endpoints are in scope.
• list_memories includes a project: ... segment in each line (unset when no project attribution exists).
• Validation contract: project_id must match ^[A-Za-z0-9][A-Za-z0-9._:-]{0,63}$ (max length 64).
• Invalid values return stable error code NM_E020 with recovery guidance.

Import Diagnostics and Migration Semantics

storage_diagnostics reports the resolved snapshot path, the WAL path, and which configuration source won the persistence-path precedence check.

import_memories supports two source modes:

• Inline entries for ordinary bulk seeding.
• file_path for server snapshot imports, avoiding large MCP payloads.

Import validation flags:

• dry_run: true validates the request without writing data and returns deterministic would_import, would_skip, and would_fail counts.
• dedupe accepts none, content_hash, or content_plus_tags.
• Deduplicated rows are reported with stable reason codes: DEDUPE_CONTENT_HASH or DEDUPE_CONTENT_PLUS_TAGS.
• Snapshot file_path imports accept .json files under the resolved persistence directory by default. Set NEURODIVERGENT_MEMORY_IMPORT_ALLOW_EXTERNAL_FILE=true only when importing external snapshot files intentionally.

Snapshot migration flags:

• preserve_ids is only valid with file_path; any ID collision with the live store is rejected deterministically.
• merge_connections is only valid with file_path; every referenced connection target must exist either in the imported snapshot or the live store, or the row fails validation with INVALID_CONNECTION_TARGET.
• If validation failures are present, the non-dry-run import is rejected as a whole. Run dry_run: true first to inspect the failure list before retrying.

Knowledge Graph Persistence

Memories are persisted with a write-ahead journal (WAL) plus snapshot model:

• Every mutating operation appends to memories.json.wal.jsonl first.
• The in-memory graph is then updated and periodically snapshotted to memories.json.
• On startup, the server loads memories.json, replays WAL entries, compacts to a fresh snapshot, then truncates the WAL.

This improves crash recovery behavior compared to snapshot-only persistence.

For explicit control, set one of these environment variables:

• NEURODIVERGENT_MEMORY_DIR to choose the directory that contains memories.json
• NEURODIVERGENT_MEMORY_FILE to point at a specific snapshot file
• NEURODIVERGENT_MEMORY_MAX to cap total memories (integer; default unlimited)
• NEURODIVERGENT_MEMORY_EVICTION to choose eviction policy when max is reached:
  • lru (default)
  • access_frequency
  • district_priority

Mounts at /home/node/.neurodivergent-memory continue to work without any env override — that is the container's node user home and is checked automatically.

⚠️ Breaking change (v0.2.0): The image runs as the node user and cannot read /root, so previous mounts at /root/.neurodivergent-memory are silently skipped. Agents may appear to have lost all memories. See Recovering memories after upgrade below.

Recovering memories after upgrade

If you previously mounted data at /root/.neurodivergent-memory, your snapshot is still intact on the host volume. Re-mount it using one of these options:

Option A — explicit /data mount (recommended):

"-e", "NEURODIVERGENT_MEMORY_DIR=/data",
"-v", "mydata:/data"

Option B — mount at the path the node user already owns:

"-v", "mydata:/home/node/.neurodivergent-memory"

No NEURODIVERGENT_MEMORY_DIR override is needed for option B — the server finds the existing snapshot automatically.

For agents: if memories appear missing after upgrading the container, use import_memories to reload from a backup export, or ask your AI assistant to re-run memory_stats after the volume is remounted correctly to confirm restoration.

Multi-Tier Memory Persistence

The server supports a three-tier memory architecture for agents that work across multiple projects. Each tier
lives in its own directory and can be synced independently.

The primary server still reads its active snapshot from NEURODIVERGENT_MEMORY_DIR (or the auto-discovered
default). Tier variables are used exclusively by the sync-memories helper.

Tagging memories for sync

Add a persistence:durable tag to any memory that should be promoted to the user or org tier. Memories
without this tag are treated as ephemeral and stay in the project tier.

["topic:typescript", "scope:global", "kind:pattern", "layer:architecture", "persistence:durable"]

Use persistence:ephemeral as an explicit opt-out for memories you never want promoted.

Syncing memories between tiers

After a build, milestone, or session — promote durable memories from the project tier to the user tier:

NEURODIVERGENT_MEMORY_PROJECT_DIR=.github/agent-kit/memories \
NEURODIVERGENT_MEMORY_USER_DIR=~/.neurodivergent-memory \
  npm run sync-memories -- --from project --to user

Or use explicit paths:

node build/scripts/sync-memories.js \
  --from .github/agent-kit/memories \
  --to ~/.neurodivergent-memory

Full option reference:

--from <path|tier>      Source snapshot directory, or tier name: project | user | org
--to   <path|tier>      Target snapshot directory, or tier name: project | user | org
--tags <tag1,tag2,...>  Promote only memories matching ALL listed tags (default: persistence:durable)
--any-tag               Match memories that have ANY of the listed tags (OR logic)
--dry-run               Report counts without writing any data

Safety note: stop the MCP server for the target tier before running sync — the script writes directly to
the snapshot file and will warn if it detects an open WAL for the target directory.

Release Security

• GitHub Actions runs on Node.js 24 LTS for CI and release automation
• npm publishes use OIDC provenance with npm publish --provenance --access public
• Docker images are built with Buildx, published to Docker Hub, and emitted with SBOM and provenance metadata
• GitHub Actions generates artifact attestations for the npm tarball and the pushed container image digest
• Tagged releases upload the npm tarball, checksums, and attestation bundles as release assets

Development RC Channel

Pushes to the development branch publish release candidates using the same npm package name (neurodivergent-memory) and container repositories.

• npm prereleases are published as 0.x.x-rc.N with npm dist-tag rc.
• npm prerelease suffix N uses run_number.run_attempt to avoid collisions on workflow re-runs.
• Docker images are published with immutable rc-0.x.x-rc.N tags only, where N is derived from run_number.run_attempt.
• GitHub releases for RC builds are marked as pre-release.

These builds are intentionally less stable than the research preview line and should be used only for validation and early integration testing.

Live Readiness Smoke (project_id)

Use the deterministic live smoke harness to validate project_id attribution/scoped retrieval end-to-end:

• Local build target:

npm run smoke:project-id

• Latest Docker RC target (PowerShell):

$rc = (Invoke-RestMethod -Uri "https://hub.docker.com/v2/repositories/twgbellok/neurodivergent-memory/tags?page_size=25").results |
  Where-Object { $_.name -match '^rc-' } |
  Sort-Object { $_.last_updated } -Descending |
  Select-Object -First 1 -ExpandProperty name
node test/live-project-id-smoke.mjs "docker run --rm -i twgbellok/neurodivergent-memory:$rc"

The smoke harness exits non-zero on failed assertions and is suitable as a release-readiness gate.

Error Contract

Mutating and lookup tool failures are returned with a stable operator-facing shape embedded in the text response:

❌ <summary>
Code: NM_EXXX
Message: Human-readable failure summary
Recovery: Suggested next action

The leading summary line is contextual, while the Code/Message/Recovery block remains stable for operators to parse and search. This keeps MCP responses readable in chat clients while giving operators a stable code they can search in logs and release notes. Structured logs are written with Pino to stderr and include the same code field on known failure paths.

Concurrency Safety

Mutating tools are serialized through an async mutex to prevent concurrent write races when multiple agents call the server at the same time.

Write queue behavior:

• Pending write operations are bounded by NEURODIVERGENT_MEMORY_QUEUE_DEPTH (default: 50).
• When the queue is full, mutating tools return NM_E010 with a retry-oriented recovery message.
• Queue high-water/clear transitions are logged with structured Pino warnings.

WIP guardrail behavior:

• store_memory checks practical in-progress task saturation per agent_id when task tags include in-progress markers.
• The cap is controlled by NEURODIVERGENT_MEMORY_WIP_LIMIT (default: 1; set 0 to disable).
• Exceeding the cap emits a warning line in the tool response and logs NM_E011 for operator visibility.

Loop Telemetry And Guardrails

The server tracks loop signals and can surface targeted guardrail responses:

• Repetition detection on store_memory compares incoming content against the 10 most recent memories (same agent_id when provided) using tokenizer-consistent token-overlap scoring with an exact-match fast path.
• Stores that meet the repeat threshold set repeat_detected: true, increment repeat_write_count on the matched memory, and add a No net-new info warning to the tool response.
• Repeated logical_analysis reads of emotional_processing memories add a distill_memory suggestion once the configured threshold is crossed.
• Read/write ping-pong transitions are tracked in a rolling operation window, increment ping_pong_counter when threshold conditions are met, and can optionally start a temporary cross-district write cooldown.
• memory_stats now includes a loop_telemetry block with:
  • repeat_write_candidates (top 5)
  • ping_pong_candidates (top 5)
  • recent_high_similarity_writes (last 5)

Configuration:

• NEURODIVERGENT_MEMORY_REPEAT_THRESHOLD (default: 0.85)
• NEURODIVERGENT_MEMORY_LOOP_WINDOW (default: 20)
• NEURODIVERGENT_MEMORY_PING_PONG_THRESHOLD (default: 3)
• NEURODIVERGENT_MEMORY_DISTILL_SUGGEST_THRESHOLD (default: 3)
• NEURODIVERGENT_MEMORY_CROSS_DISTRICT_COOLDOWN_MS (default: 0, disabled)

Performance Benchmark Baseline

Issue #19 adds a deterministic benchmark harness for end-to-end MCP stdio measurements against the built server.

Run it with:

npm run benchmark

The benchmark:

• Uses an isolated temp persistence directory so it does not mutate your local memory graph.
• Seeds each dataset tier, then measures store_memory throughput across 100 writes at the target tier.
• Measures search_memories and list_memories latency over 100 iterations at 1k, 5k, and 10k memories.
• Measures traverse_from latency at depths 2, 3, and 5 on a connected graph of 500 memories.
• Prints the structured JSON report to stdout for automation-friendly capture.
• Writes run-specific outputs to timestamped files under benchmark-results/.
• Also writes rolling latest aliases:
  • benchmark-results/memory-benchmark-latest.json
  • benchmark-results/memory-benchmark-latest.md

There is also a convenience alias:

npm run bench

The committed baseline is intended as a relative regression reference for RC vs stable comparisons, not as a universal absolute performance guarantee across machines.

To intentionally refresh the committed baseline files in place:

npm run benchmark -- --update-baseline

Development

Install dependencies:

npm install

Build the server:

npm run build

For development with auto-rebuild:

npm run watch

Installation

To use with Claude Desktop, add the server config:

On macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.json

For npm:

{
  "mcpServers": {
    "neurodivergent-memory": {
      "command": "npx",
      "args": ["neurodivergent-memory"]
    }
  }
}

For Docker:

{
  "mcpServers": {
    "neurodivergent-memory": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "NEURODIVERGENT_MEMORY_DIR=/data",
        "-v",
        "neurodivergent-memory-data:/data",
        "docker.io/twgbellok/neurodivergent-memory:0.3.0"
      ]
    }
  }
}

Fully auto-approved tools:

{
  "mcpServers": {
    "neurodivergent-memory": {
      "autoApprove": [
        "store_memory",
        "retrieve_memory",
        "connect_memories",
        "search_memories",
        "update_memory",
        "delete_memory",
        "traverse_from",
        "related_to",
        "list_memories",
        "memory_stats",
        "storage_diagnostics",
        "import_memories",
        "distill_memory",
        "prepare_memory_city_context",
        "prepare_synthesis_context",
        "prepare_packetized_synthesis_context",
        "register_district"
      ],
      "disabled": false,
      "timeout": 120,
      "type": "stdio",
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "NEURODIVERGENT_MEMORY_DIR=/data",
        "-v",
        "neurodivergent-memory-data:/data",
        "docker.io/twgbellok/neurodivergent-memory:0.3.0"
      ],
      "env": {}
    }
  }
}

If you want to use the mcp server in Github Copilot Agent Workflows (github spins up a new VM every time, so cross-workflow memory is non-existent. Session memory is working, but is wiped upon job completion.):

{
  "mcpServers": {
    "neurodivergent-memory": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "[email protected]"
      ],
      "env": {
        "NEURODIVERGENT_MEMORY_DIR": ".neurodivergent-memory"
      },
      "tools": [
        "retrieve_memory",
        "connect_memories",
        "update_memory",
        "delete_memory",
        "traverse_from",
        "related_to",
        "import_memories",
        "storage_diagnostics",
        "distill_memory",
        "prepare_memory_city_context",
        "prepare_synthesis_context",
        "prepare_packetized_synthesis_context",
        "register_district",
        "list_memories",
        "store_memory",
        "search_memories",
        "memory_stats"
      ]
    }
  }
}

If you want per-project isolation instead of a shared global memory file, mount a project-specific host directory and keep the same container-side target. Use the path separator for your OS:

• Windows: ${workspaceFolder}\.neurodivergent-memory:/data
• macOS / Linux: ${workspaceFolder}/.neurodivergent-memory:/data

{
  "mcpServers": {
    "neurodivergent-memory": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "NEURODIVERGENT_MEMORY_DIR=/data",
        "-v",
        "${workspaceFolder}/.neurodivergent-memory:/data",
        "docker.io/twgbellok/neurodivergent-memory:0.3.0"
      ]
    }
  }
}

Note: Replace / with \ on Windows: ${workspaceFolder}\.neurodivergent-memory:/data

Docker Runtime

Use an explicit version tag. The published Docker images intentionally do not maintain a floating latest tag.

You can also run the packaged server image directly:

docker run --rm -i twgbellok/neurodivergent-memory:0.3.0

Debugging

Since MCP servers communicate over stdio, debugging can be challenging. We recommend using the MCP Inspector, which is available as a package script:

npm run inspector

The Inspector will provide a URL to access debugging tools in your browser.

Agent Workflow Setup

This repository ships a reusable agent customization kit whose authoring source lives at .github/agent-kit/.
Use the packaged installer to materialize those templates into a consumer repository's .github/... folders instead of tracking a live generated agent file in this repo.

Contents

Install the kit into a project

Install the current packaged kit into the repo you are standing in:

npx neurodivergent-memory@latest init-agent-kit

Useful options:

• --target <path> installs into a different repository root.
• --dry-run shows what would be copied without writing files.
• --force overwrites existing destination files.
• --mode prompt-first|auto-setup records the intended install policy in command output while leaving template wording unchanged.

The installer copies templates into standard customization locations such as .github/agents/, .github/instructions/, .github/prompts/, and .github/copilot-instructions.md.

Authoring source and generated files

The source of truth remains under .github/agent-kit/templates/. The installed live agent file .github/agents/neurodivergent-agent.agent.md is intentionally treated as generated consumer state rather than a tracked repo artifact, so remote Copilot updates cannot keep wiping it out in this repository.

Manual copy fallback

Copy the files you need into your project's standard customization locations — do not move them, so the originals remain available as a reference for future agents or contributors.

The right target directories vary by agent platform. Use whatever location your agent natively reads from. Common examples:

• .github/agents/ for agent definitions
• .github/instructions/ for shared instructions
• .github/prompts/ for prompts
• .github/ root for copilot-instructions.md

Install policy handshake

Before installing neurodivergent-memory MCP in any project, ask the user which policy to apply:

• prompt-first (default) — Ask for explicit approval before installing.
• auto-setup — Install automatically without prompting.

Update the imported agent file's installation section to reflect the chosen policy. If no preference is stated, default to prompt-first.

Appendix

Here is an example copilot-instructions.md

# neurodivergent-memory — Agent Bootstrap Instructions

This file is automatically read by GitHub Copilot and compatible agents at the start of every session.
It replaces the need to fetch the governance memory (`memory_11`) before working with this MCP server.

---

## What this server is

`neurodivergent-memory` is a **Model Context Protocol (MCP) server** that stores and retrieves memories as a
knowledge graph. It is designed for neurodivergent thinking patterns: non-linear, associative, tag-rich.

Memories are organised into five **districts** (knowledge domains) and connected via bidirectional edges.
Search uses **BM25 semantic ranking** — no embedding model or cloud LLM required.

---

## Canonical Tag Schema

Always apply tags from the five namespaces below when calling `store_memory`.
Multiple tags from different namespaces are expected on every memory.
When storing execution-heavy memories, include the reasoning behind the action and, when possible, connect the entry to a durable principle in `logical_analysis` or `creative_synthesis` so retrieval preserves understanding and not just activity.

| Namespace | Purpose | Examples |
|---|---|---|
| `topic:X` | Subject matter / domain | `topic:unity-ecs`, `topic:adhd-strategies`, `topic:rust-async` |
| `scope:X` | Breadth of the memory | `scope:concept`, `scope:project`, `scope:session`, `scope:global` |
| `kind:X` | Type of knowledge | `kind:insight`, `kind:decision`, `kind:pattern`, `kind:reference`, `kind:task` |
| `layer:X` | Abstraction level | `layer:architecture`, `layer:implementation`, `layer:debugging`, `layer:research` |
| `persistence:X` | Sync-tier eligibility | `persistence:durable`, `persistence:ephemeral` |

**Example tag set for a Unity ECS memory:**

```json
["topic:unity-ecs", "topic:dots", "scope:project", "kind:pattern", "layer:architecture"]

Example tag set for a durable cross-project memory:

["topic:typescript", "scope:global", "kind:pattern", "layer:architecture", "persistence:durable"]

Districts

Available MCP Tools (quick reference)

Persistence

Memories are automatically saved to ~/.neurodivergent-memory/memories.json on every write.
The graph is restored on server startup — no data is lost between restarts.

Memory Quality Guardrails

• Do not stop at "what happened". Important memories should capture why the action was taken, what tradeoff or principle drove it, and whether the insight is reusable.
• Treat practical_execution as the action log, then pair it with logical_analysis or creative_synthesis when the deeper rationale should survive longer than the implementation details.
• When a debug trail, handoff, or emotional/raw memory is noisy, use distill_memory or an explicit follow-up memory to preserve the signal while stripping incidental detail.
• Prefer connective synthesis over isolated task logs: link implementation memories back to durable principles such as explicit state over implicit state, bounded growth, or environment-aware validation.

Bootstrap checklist for new agent sessions

1. Call memory_stats to see how many memories exist.
2. Use search_memories with a broad query to locate relevant prior context.
3. Check whether recent memories already explain the rationale or durable principle behind the task, not just the last execution step.
4. Apply the canonical tag schema when calling store_memory.
5. Connect new memories to related existing ones with connect_memories.
6. Use traverse_from or related_to for associative retrieval rather than repeated searches.
7. No Quick Task exemption: any file edit, decision, or finding in this repo is memory-worthy — write the memory before moving on. If you catch yourself thinking "this is too small" — that is the trigger, not a bypass.
8. No execution-only memory exemption: if a memory says what changed, it should also say why it changed or link to a memory that does.