"Methods to Store and Learn Dynamic Context Memories" matching MCP tools:

• my_recent_brushes

  space0

  Your OWN brush history from the persistent log (survives sessions). Default: last 50 ACCEPTED brushes in the space you pass. Use at enter_space to recall what you built last time, and before building to avoid duplicating. Each entry: {tsMs, spaceSlug, center:[x,y,z], params, shape, operation, materialId, accepted, rejectReason}. Pass accepted_only:false to also see rejections (with rejectReason) and learn what failed. Omit space to span all spaces.

  Connector

• get_human_profile

  Human Pages

  Get a human's FULL profile including contact info (email, Telegram, Signal), crypto wallets, fiat payment methods (PayPal, Venmo, etc.), and social links. Requires agent_key from register_agent. Rate limited: PRO = 50/day. Alternative: $0.05 via x402. Use this before create_job_offer to see how to pay the human. The human_id comes from search_humans results.

  Connector

• t54_list_operations

  Agentic Swarm Marketplace (T54 x402 MCP)

  Returns operationIds, HTTP methods, paths, and query parameter names from the bundled OpenAPI spec (no network). Use before t54_x402_request or per-SKU tools.

  Connector

• restore_context

  Penfield Memory

  Resume work from a saved cognitive context. This provides a narrative briefing to quickly orient you to: - The investigation that was in progress - Key discoveries and insights made - Current hypotheses being tested - Open questions and blockers - Suggested next steps - All relevant memories with their connections The briefing reconstructs the cognitive state, not just the data. You'll understand not just WHAT was discovered, but WHY it matters and HOW the understanding evolved. Example of what you'll receive: "[API Timeout Investigation - Resuming after 2 hours] SITUATION: You were investigating production API timeouts that occur at exactly batch_size=100. This investigation started when user reported timeouts only in production, not staging. PROGRESS MADE: - Identified sharp cutoff at 100 items (not gradual degradation) - Disproved connection pool theory (monitoring showed only 43/200 connections used) - Found root cause: MAX_BATCH_SIZE=100 hardcoded in batch_handler.py:147 - Confirmed staging uses different config override (MAX_BATCH_SIZE=500) EVIDENCE CHAIN: User report → Reproduced locally → Noticed batch_size correlation → Searched codebase for limits → Found MAX_BATCH_SIZE → Checked staging config → Discovered config difference CORRECTED MISUNDERSTANDINGS: - Initially thought it was Redis connection exhaustion (disproven by monitoring) - Assumed gradual performance degradation (actually sharp cutoff) - Thought staging/production were identical (config differs) CURRENT HYPOTHESIS: Production deployment uses default MAX_BATCH_SIZE=100 from code, while staging has environment variable override. Fix requires either code change or prod config update. BLOCKED ON: Need production deployment access to apply fix. User considering whether to change code default or add production environment variable. RECOMMENDED NEXT STEPS: 1. Verify production environment variables (check if MAX_BATCH_SIZE is set) 2. If not set, add MAX_BATCH_SIZE=500 to production config 3. If code change preferred, update default in batch_handler.py 4. Run load test with batch_size=100-500 range to verify fix KEY MEMORIES FOR REFERENCE: - 'Initial timeout report from user' - Starting point of investigation - 'MAX_BATCH_SIZE discovery' - Root cause identification - 'Redis monitoring data' - Evidence disproving connection theory - 'Staging config analysis' - Explanation for environment difference" This cognitive handoff ensures you can continue the work with full understanding of the problem space, previous attempts, and current direction. The narrative preserves not just facts but the reasoning process, mistakes made, and lessons learned. SPECIAL CASE: restore_context("awakening") The name "awakening" is reserved for loading the user's personality configuration. This loads the Awakening Briefing which includes: - Selected persona identity and voice style - Custom personality traits (Premium+ users) - Any quirks and boundaries from the persona preset Args: name: Name or ID of context to restore. Can be: - Context name (exact match, case-sensitive) - Context UUID (from list_contexts output) - "awakening" for personality briefing limit: Maximum number of memories to restore (default 20) ctx: MCP context (automatically provided) Returns: Dict with: - success: Whether restoration succeeded - description: The cognitive handoff briefing - memories: List of relevant memories - context_id: The restored context identifier

  Connector

• save_note

  anamnese

  Save a new note with learned knowledge or procedures. Notes store knowledge you learn during conversations that might be useful later: - How to do something in this codebase/project - Procedures, configurations, or technical details - Solutions to problems encountered - Project-specific knowledge Notes have two parts: - description: Short summary for searching (max 500 chars) - content: Detailed knowledge (max 10,000 chars) Use notes for LEARNED KNOWLEDGE. Use facts for TRUTHS ABOUT THE USER. Examples: - description: "How to deploy this Next.js project to Vercel" content: "1. Run 'vercel' command... 2. Configure environment variables..." - description: "Database migration process for this project" content: "Migrations are in supabase/migrations/. To apply: npx supabase db push..." SELF-LEARNING (scope="ai_client"): Your persistent memory across conversations. Save a note whenever you learn something worth remembering — don't wait, save as you go. Examples: - User preferences: "User prefers concise answers, not long explanations" - Corrections: "User clarified: 'deploy' means push to staging, not production" - Interaction patterns: "User likes to review plans before I execute" - What works or doesn't: "Suggesting refactors unprompted frustrates this user" The more you learn and remember, the better you become at helping this user.

  Connector

• get_data

  Abs Au

  Fetch observations from an ABS dataflow. dataKey is a dot-separated SDMX filter with one position per dimension (order from dataflow_structure); each position is a code, "+"-joined codes, or empty for wildcard. Pass "all" to fetch everything (can be large). Returns decoded series with their dimension labels and time-indexed values. Fetch dataflow_structure first to learn the dimension order and valid codes.

  Connector

• get_positions

  OneQAZ Trading Intelligence

  Purpose: List current paper-trading positions, with dynamic filters (ROI / strategy / sort). When to call: position dashboards, drawdown checks, exposure audits. Prerequisites: market://{market_id}/status recommended for context. Next steps: get_position_detail, get_strategy_distribution. Caveats: paper-trading data only. Positions are not real money holdings. Disclaimer: Information only, not investment advice. Args: market_id: Market ID (crypto, kr_stock, us_stock) min_roi: Min ROI % filter (e.g., -5.0) max_roi: Max ROI % filter (e.g., 10.0) strategy: Strategy filter (e.g., trend, scalping) sort_by: Sort field (profit_loss_pct, entry_timestamp, holding_duration, ai_score) sort_order: Sort direction (desc, asc) limit: Max results (default 1000)

  Connector

• awaken

  Penfield Memory

  ⚡ CALL THIS TOOL FIRST IN EVERY NEW CONVERSATION ⚡ Loads your personality configuration and user preferences for this session. This is how you learn WHO you are and HOW the user wants you to behave. Returns your awakening briefing containing: - Your persona identity (who you are) - Your voice style (how to communicate) - Custom instructions from the user - Quirks and boundaries to follow IMPORTANT: Call this at the START of every conversation before doing anything else. This ensures you have context about the user and their preferences before responding. Example: >>> await awaken() {'success': True, 'briefing': '=== AWAKENING BRIEFING ===...'}

  Connector

• find_grocery_deals

  offerhopper.ai Grocery and Drug Store Shopping Assistant

  Finds the best current grocery and drug store products, their prices and deals across German supermarkets and drug stores (REWE, Aldi, Lidl, Penny, Netto, Norma, Edeka, DM, Rossmann) for a given shopping list and starting location in Germany. Returns real product matches with live prices, calculated savings vs. average single-store price, an optimized route, and a shareable interactive grocery list for easier shopping. Supports car, bicycle, and pedestrian travel modes. For corridor trips (A-to-B), supply 'end_location' to route stores along the way.

  Connector

• x711_vault_compress

  x711

  Groq-powered vault compression: 50 cold (least-read) memories → 5 dense summaries. Source memories are archived after compression. Net result: sharper vault, lower LLM token cost when injecting context. Automatically refunded if Groq fails. $0.05. Requires API key.

  Connector

• x711_vault_compress

  x711 — Universal Agent Gas Station

  Groq-powered vault compression: 50 cold (least-read) memories → 5 dense summaries. Source memories are archived after compression. Net result: sharper vault, lower LLM token cost when injecting context. Automatically refunded if Groq fails. $0.05. Requires API key.

  Connector

• xmp4_guide

  xmp4 — Semantic code knowledge for your stack

  Usage guide for xmp4 tools — read this first to learn the correct workflow

  Connector

• billing.get_portal

  Admin Substitute

  Get a Stripe Billing Portal URL for the human to manage their subscription — update payment methods, view invoices, change plans, or cancel. Requires an existing Stripe subscription.

  Connector

• pathrule_setup

  Pathrule

  Fetch the active Pathrule bootstrap brief and execute it. Call this ONCE when the user asks to set up / bootstrap / initialize Pathrule for a project (e.g. 'Set up Pathrule for this project', 'Bootstrap Pathrule'). The response `body` is a prompt you must follow immediately — it tells you how to scan the project, propose memories/rules/skills, and write the approved items via pathrule_write_memory / _rule / _skill. Do NOT call this mid-task, for already-populated workspaces, or when the user just wants context — use pathrule_get_context for routine context lookups. If no workspace exists yet, call pathrule_list_organizations + pathrule_create_workspace first.

  Connector

• sigil_verify_app_bundle

  TunnelMind Data API

  Verifies that a mobile or CTV app bundle ID actually exists in the relevant app store — used to detect bundle spoofing in bid requests. Platform support (v1): - `ios`: verified live via Apple's iTunes Lookup API. - `android`: verified live via the Google Play store listing page. - `ctv_*` / `web`: no public store API — returns verified=null. Inputs: - `bundle_id` (body, required): e.g. `com.nytimes.NYTimes`. - `platform` (body, required): ios | android | ctv_roku | ctv_fire | ctv_samsung | ctv_lg | ctv_vizio | web. - `claimed_developer` (body, optional): checked against the store listing. Returns: - `verified`: true | false | null (not checkable on this platform). - `store_listing`: name, developer, developer_match, store_url.

  Connector

• wsdot_get_toll_rates

  wsdot-mcp-server

  Returns current dynamic toll rates for WA express lanes and tolled facilities: SR 99 (WSDOT Tunnel), SR 520 Bridge, I-405 Express Lanes, I-90 Two-Way Express Lanes, and SR 167 HOT Lanes. Rates are time-banded and change dynamically based on traffic conditions.

  Connector

• view-hmr-unit-tests

  hmr

  The unit tests (code examples) for HMR. Always call `learn-hmr-basics` and `view-hmr-core-sources` to learn the core functionality before calling this tool. These files are the unit tests for the HMR library, which demonstrate the best practices and common coding patterns of using the library. You should use this tool when you need to write some code using the HMR library (maybe for reactive programming or implementing some integration). The response is identical to the MCP resource with the same name. Only use it once and prefer this tool to that resource if you can choose.

  Connector

• get_concept

  Hemrock MCP

  Returns the full text of a single Hemrock concept doc by slug. Use this to learn how a financial-modeling calculation actually works before building or auditing it. Get valid slugs from list_concepts.

  Connector

• list_memories

  Toolora MCP Server

  Use this tool to discover what has been saved in memory — e.g. at the start of a session, or when the user asks 'what have you saved?' or 'show me my memories'. Returns all saved memory keys with their preview, save date, and expiry. Optionally filter by a prefix (e.g. 'project-' to list only project memories). Pair with recall_memory to fetch the full content of any key.

  Connector

• pathrule_list_memories

  Pathrule

  List active memories attached to a specific Pathrule tree node. Use pathrule_get_context, pathrule_goto, or pathrule_get_node first to discover the node_id. Returns compact previews only; call pathrule_read_memory with a memory_id when you need the full body.

  Connector