"Understanding or Using Memory Lists" matching MCP tools:

• fetch

  Penfield Memory

  [ChatGPT Connector compat] Fetch memory by ID. Exists to satisfy ChatGPT Deep Research's required `search`/`fetch` tool contract. Native MCP clients should fetch via `recall` + memory_id, or use the API's GET /memories/{id} endpoint directly. Returns a single memory with citation support (id, title, url, text fields). Args: id: Memory UUID to fetch ctx: MCP context Returns: Dict with id, title, url, text, metadata fields

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• searchMemory

  GrowthKit Revenue Intelligence

  Search long-term memory using semantic similarity. ALWAYS SEARCH BEFORE ANSWERING marketing/strategy questions. Use short, specific keywords as queries. Available chapters: icp, strategy, campaigns, analytics, brand, competitors, learnings, general, pipeline, signals, playbook. TAG FILTERING: Memories are auto-tagged. Use metadata_filter with tags key to filter (e.g. metadata_filter: { chapter: 'campaigns', tags: 'linkedin' }).

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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connect related memories using these relationship types: ## Knowledge Evolution - **supersedes**: This replaces → outdated understanding - **updates**: This modifies → existing knowledge - **evolution_of**: This develops from → earlier concept ## Evidence & Support - **supports**: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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• suggest_terms

  Arco Lexicon

  Scans a block of text against all published Arco Lexicon terms using deterministic string matching — no LLM calls. Returns two lists: terms whose canonical names appear explicitly in the text (detected), and terms whose concepts are present but whose canonical names are absent (suggested). Maximum 10,000 characters. Use this to audit an article or passage for correct and complete Arco terminology. Use verify_alignment instead when you want a scored alignment report rather than a term discovery list.

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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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• list_reminder_lists

  local-mcp

  Lists the lists (folders) in Apple Reminders (Reminders.app) on this Mac. For Microsoft To Do use todo_list_lists instead.

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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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• list_reference

  Worldbank Poverty

  Reference / metadata for the PIP dataset. table="versions" lists available data releases & PPP rounds; table="aux_list" lists all auxiliary tables; any other value (e.g. "countries", "regions", "poverty_lines", "indicators", "dictionary") returns that auxiliary table. Use to look up valid country codes, region mappings, or supported indicators.

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• agent_history

  AgentAegis

  Lists your recent scans (scan_id, tool, target, status, time) so you can retrieve or chain from a prior result. Optional limit/tool/target/since filters. Free to call.

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• memory_verify

  recognitium

  Boot-time integrity check of the agent's memory against its sealed biography. Send entries: [{name, data_hash}] for every memory currently held. Per memory: VERIFIED, ROLLBACK (matches an older sealed state), FOREIGN_EDIT (matches none), or UNSEALED. Verdict INTACT or TAMPER-EVIDENT; quarantine anything not VERIFIED. Sealed names the agent failed to report are returned too. Metered auth, no tick cost.

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• arxiv_get_metadata

  arxiv-mcp-server

  Get full metadata for one or more arXiv papers by ID. Use when you have known IDs from citations, prior search results, or memory.

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• info_coin_get_coin_info

  Gate Info MCP

  Single-target lookup: ticker, name, or contract→rows. Chain fixes address clashes. Filtered lists→search_coins. Boards→get_coin_rankings.

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• list_blog_categories

  Walnai Website MCP

  Lists all Walnai blog categories with their slug, name, and description. Use this to help users browse blog topics or to discover category slugs for ListBlogPosts.

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• get_trident_spec

  trident

  Returns the complete Trident 2D specification including grammar, syntax rules, coordinate system, containers, nodes, connections, shapes, and icon reference. Use this when you need deep understanding of the Trident DSL.

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• fetch_confluence_page

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

  Fetch a Confluence page and return its content as clean Markdown. Accepts a numeric page_id or a full page URL. Optionally lists direct child pages. BYOK — credentials transit in-memory only, never stored.

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• todo_list_lists

  local-mcp

  Lists Microsoft To Do task lists. Requires Microsoft account in Reminders sync (System Settings → Internet Accounts → Microsoft Exchange → enable Reminders).

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• forget_memory

  haus

  Forget (soft-delete) a memory by id. Use when a memory is superseded or no longer relevant.

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• tcp_throughput

  calc.engineer

  Calculate maximum TCP throughput using the Bandwidth-Delay Product (BDP) formula. Given link bandwidth and round-trip latency, computes the BDP (maximum in-flight data), achievable throughput with a given TCP window size, link utilization percentage, and recommended window size for full utilization. Critical for diagnosing slow transfers over high-latency links (WAN, VPN, satellite), tuning TCP buffers, and understanding why a 1Gbps link may only deliver 25Mbps with default 64KB windows. Applies to iperf testing, WAN optimization, and network capacity planning.

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• loops_list_mailing_lists

  loops

  List all mailing lists in the Loops account (id + name). Loops API: GET /v1/lists.

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• browse

  cards

  Navigate the database hierarchy: no arguments lists sports with card counts; sport lists years; sport + year lists products; product_id lists sets.

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