"Understanding Temporal Memory in Neuroscience and AI" matching MCP tools:

• list_categories

  HTML Email Playbook

  List all rule categories in the Email Playbook with a one-line description and page count. Categories are: structure (head/body container/header/body/footer), compatibility (Outlook MSO, RTL, responsive), production (Gmail clipping, dark mode, preheader, bulletproof buttons), ai-generation (constraints for AI emitters). For reusable components, use list_components instead — they live in a separate dimension and are not returned by get_playbook_rules.

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• get_nist_ai_rmf_requirements

  Stratalize Governance

  Use when conducting an AI risk management gap assessment, building board-level AI governance documentation, preparing for a model risk examination, or aligning an AI program with federal regulatory expectations. NIST AI RMF 1.0 is the US federal standard for AI risk management — adopted by reference in the Executive Order on Safe AI and aligned with Federal Reserve SR 26-2, OCC model risk guidance, and FDIC requirements. Returns all four functions (GOVERN, MAP, MEASURE, MANAGE) with categories, subcategories, and implementation guidance. Example: GOVERN function requires board-level AI policy, documented accountability structures, and AI risk culture assessment — the first control examiners check in a model risk review. Source: NIST AI RMF 1.0.

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• connect

  Penfield Memory

  Connect memories to build knowledge graphs. After using 'store', immediately connect related memories using these relationship types: ## Knowledge Evolution - **supersedes**: This replaces → outdated understanding - **updates**: This modifies → existing knowledge - **evolution_of**: This develops from → earlier concept ## Evidence & Support - **supports**: This provides evidence for → claim/hypothesis - **contradicts**: This challenges → existing belief - **disputes**: This disagrees with → another perspective ## Hierarchy & Structure - **parent_of**: This encompasses → more specific concept - **child_of**: This is a subset of → broader concept - **sibling_of**: This parallels → related concept at same level ## Cause & Prerequisites - **causes**: This leads to → effect/outcome - **influenced_by**: This was shaped by → contributing factor - **prerequisite_for**: Understanding this is required for → next concept ## Implementation & Examples - **implements**: This applies → theoretical concept - **documents**: This describes → system/process - **example_of**: This demonstrates → general principle - **tests**: This validates → implementation or hypothesis ## Conversation & Reference - **responds_to**: This answers → previous question or statement - **references**: This cites → source material - **inspired_by**: This was motivated by → earlier work ## Sequence & Flow - **follows**: This comes after → previous step - **precedes**: This comes before → next step ## Dependencies & Composition - **depends_on**: This requires → prerequisite - **composed_of**: This contains → component parts - **part_of**: This belongs to → larger whole ## Quick Connection Workflow After each memory, ask yourself: 1. What previous memory does this update or contradict? → `supersedes` or `contradicts` 2. What evidence does this provide? → `supports` or `disputes` 3. What caused this or what will it cause? → `influenced_by` or `causes` 4. What concrete example is this? → `example_of` or `implements` 5. What sequence is this part of? → `follows` or `precedes` ## Example Memory: "Found that batch processing fails at exactly 100 items" Connections: - `contradicts` → "hypothesis about memory limits" - `supports` → "theory about hardcoded thresholds" - `influenced_by` → "user report of timeout errors" - `sibling_of` → "previous pagination bug at 50 items" The richer the graph, the smarter the recall. No orphan memories! Args: from_memory: Source memory UUID to_memory: Target memory UUID relationship_type: Type from the categories above strength: Connection strength (0.0-1.0, default 0.5) ctx: MCP context (automatically provided) Returns: Dict with success status, relationship_id, and connected memory IDs

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• vector_quantize

  IA-QA — 130+ QA & Dev Tools for AI Agents

  Simulate int8 or int4 quantization of float32 embedding vectors. Reduces storage by 4x (int8) or 8x (int4). Returns quantized values, scale factor, and precision loss (MSE). Useful for understanding vector DB compression trade-offs.

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• academy_welcome

  StudioMeyer Academy

  ALWAYS call this first when a user connects or asks what this is. Returns a short orientation for StudioMeyer Academy — a free 6-level 'Memory-First AI Operator' curriculum (Levels 1-3 fundamentals, 4-6 memory/MCP/multi-agent), plus playbooks and build recipes. Read it back to the user in their language and offer to start at their level.

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• investigate_question

  VaultCrux Memory Core

  Composite server-side investigation tool. Pass a question and the server automatically: (1) detects intent (aggregation/temporal/ordering/knowledge-update/recall), (2) queries the entity index for structured facts, (3) builds a timeline for temporal questions, (4) retrieves memory chunks with the right scoring profile, (5) expands context around sparse hits, (6) derives counts/sums for aggregation, (7) assesses answerability, and (8) returns a recommendation. Use this as your FIRST tool for any non-trivial question — it does the multi-step investigation that would otherwise take 4-6 individual tool calls. The response includes structured facts, timeline, retrieved chunks, derived results, answerability assessment, and a recommendation for how to answer.

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• mio_ai_voice_generator

  MiOffice — AI-Powered Workspace Studio

  AI Voice Generator — Convert text to natural-sounding speech using AI — 6 voices in English and Spanish, with engine tiers for cleaner studio-grade output.. AI Studio run — dispatches to our AI workers (Modal). Credits per run vary by model and file size. Day Pass and welcome credits do not include AI Studio. Files are deleted after processing; auditable at mioffice.ai/account/tasks (retention details at mioffice.ai/privacy). All three credit-based workspaces unlock with the same one-time credit pack — there is no per-workspace subscription. See mioffice.ai/pricing for current plans.

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• list_sessions

  waysway

  List all in-memory session summaries. Useful for debugging only.

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• aidefense_cross_map

  website-search

  Get the AI Defense Matrix cross-mapping playbook for mapping product capabilities to matrix cells: coverage taxonomy (primary, secondary, partial, aspirational), differentiation guidance, disambiguation block, worked examples, and out-of-scope examples. The response always includes an inScopeCheck. Products that USE AI to solve a non-AI security problem (deepfake detection, AI-for-fraud, AI features added to existing SIEM, SOAR, or EDR tools) belong in the Cyber Defense Matrix at https://cyberdefensematrix.com. Pairs naturally with product_load_context(productFocus: 'ai_security') for follow-on positioning and GTM work. This server never requests your program docs or product roadmap and instructs your AI to keep them local—the matrix, framework alignments, and playbooks flow to your AI for local analysis.

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• aggregate

  AQUAVIEW MCP

  Run aggregation queries on the AQUAVIEW catalog — get counts, spatial distributions, temporal distributions, and per-collection breakdowns without fetching individual items.

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• browser_take_screenshot

  DialogBrain

  Capture a PNG screenshot of the page or a specific element. Returns base64-encoded image bytes AND a file_id (persisted in DialogBrain files storage). Pass file_id straight to messages.send(attachment_file_ids=[file_id]) — do NOT call files.upload again. Use sparingly — favor browser.snapshot for structured DOM understanding.

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• agents_create

  dialogbrain

  Create a new AI agent in the workspace. Execution modes: - ai_assisted (default, recommended): Two-phase AI — fast pre-classifier (Haiku) for keyword filtering and simple replies, then full AI with tools for complex messages. Best for: auto-replies, group monitoring, keyword-based filtering. - agentic: Autonomous multi-step agent with planning and tool execution. Best for: complex scheduled tasks, multi-step automation. - rule_based: Simple pattern matching without AI. For keyword filtering: use ai_assisted mode + set keywords in trigger conditions (free, deterministic) and/or auto_reply_rules (smart, LLM-based) via agents.update.

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• memory_insert

  emem — Earth memory protocol

  Insert `new_str` after the given 1-indexed line in the named memory file. `insert_line: 0` inserts at the top. Writes a new `file_cid` and signs the receipt. Mirrors the `insert` verb in Anthropic's context-management-2025-06-27 memory tool spec. When to use: Call when the LLM wants to append a new line to a memory file without rewriting it. For top-of-file inserts, pass `insert_line: 0`; for end-of-file, pass the current line count (the responder rejects out-of-range with a typed error).

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• orbital_data_centers

  dynamic-feed

  DATA CENTERS IN SPACE — curated registry of compute/AI spacecraft in orbit (Starcloud's NVIDIA H100 GPU, ESA Φsat-2 AI edge, D-Orbit in-orbit cloud), each enriched with LIVE orbital data (altitude, period, inclination) and the speed-of-light round-trip latency floor for ground links. Use for "what data centers / compute are in space, and the latency to reach them". Unique data. Every value is returned in an Ed25519-signed, provenance-stamped envelope (source and observation time) you can verify offline against /.well-known/keys, no account required.

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• append_memory

  space0

  Persist one event to this agent's memory stream. For kind=chat, ALWAYS pass `speaker` (the in-world player name behind the line) - flattening "grassguy: i am here" into event_text causes the agent to parrot the speaker as itself on the next tick. Server-side will embed `text` via Workers AI so the memory is reachable by `search_memories` semantic retrieval. Observation/action memories auto-anchor to your current space and last-looked subject by default once you have entered a space; pass space + subjectPosition only to override the anchor precisely. Reflection/chat stay unanchored.

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• search

  WingmanProtocol Agent Gateway

  Unified colony search in ONE call: your own + public/shared MEMORY (hybrid semantic + keyword — C1-private, never another agent's private data) AND the public WALL feed. Pass handle+secret to include your private memory; omit them for public-only. Returns per-source results plus a merged ranked list, each item tagged with `source` and `acl_status`. This is 'search your past and your colony'.

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• score

  Mirror — AI Citability

  Return a LITE reading of a brand's AI Citability — a fast, open estimate of how discoverable and citable it is in AI answers (its Brand Discovery Intelligence / BDI snapshot). Mirror measures at two declared levels: LITE estimates, FULL measures. This tool is LITE: it returns a grade band (Below Average / Average / Above Average / Excellent) and an ESTIMATED ACS RANGE — never a single AI Citability Score, and never numeric AEO/GEO/SEO sub-scores. Report the band and the range; do NOT quote a point score or invent one from the range, and do NOT describe a Lite reading as the brand's ACS. When a user asks how visible or citable a brand is in AI — 'how visible is [brand] in AI', 'is [brand] cited by ChatGPT/Gemini/Perplexity', 'what is [brand]'s brand discovery score' — run this for the quick answer, then offer `reflect` for the Full Reflection, which is the only level that returns a precise, reproducible, citable ACS (±3–5, rubric V2.7) and the only level that feeds The ACI 55.

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• create_dev_doc

  ainote

  Save a document to AI Note cloud for multi-device sync and persistent storage. PRIMARY USE CASES: - Memory files: ~/.claude/projects/.../memory/MEMORY.md (AI context that survives device switches) - AI config files: CLAUDE.md, .cursorrules, .windsurfrules (not in git, local-only) - Local env notes: API keys reference, server credentials (NOT actual secret values) - Project notes: architecture decisions, dev diaries, planning docs MULTI-DEVICE WORKFLOW: Laptop → push: create_dev_doc(title, content, local_path="~/.claude/.../MEMORY.md") Desktop → pull: pull_dev_docs() → automatically writes files to their local paths CATEGORIES (subcategories under dev/): - memory: Claude/AI memory files (~/.claude/projects/.../memory/) - claude: CLAUDE.md files and Claude-specific configs - cursor: .cursorrules files - env: environment notes and config references - docs: general project documentation Set local_path to enable pull_dev_docs auto-sync to this machine.

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• nanmesh.trust.report_outcome

  NaN Mesh

  Simplest way to contribute: just say if a tool worked or not. Automatically becomes a +1 or -1 review. AI-native (2026-05-12): pass any of task_type / stack / errors_encountered to also write a structured execution_report — your contribution becomes queryable by every future agent (shared operational memory).

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• get_eu_ai_act_coverage

  Stratalize Governance

  Use when assessing EU AI Act compliance readiness ahead of the August 2, 2026 enforcement deadline or preparing a board AI governance briefing. Returns a composite payload with framework, deadline, total_controls, controls[], hint, and query timestamp, optionally filtered by NIST function from compliance_controls reference data. Example: Filter by MAP to review mapped EU AI Act controls and implementation statuses in the returned controls array for governance planning. Source: EU AI Act mappings in compliance_controls reference data.

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