"A system like ChatGPT with memory to provide past query context in conversations" matching MCP tools:

• get_knowledge_version

  marmo-ui

  Returns the MCP knowledge version: gitSha, indexedAt, componentCount, patternCount, uptimeSeconds. Call this ONCE per session before generating UI code so you know how fresh the design-system data is. Cheap to call. If gitSha is "unknown" or indexedAt is far in the past, surface that to the user before relying on the data.

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• perspective_get_preview_link

  Perspective AI

  Returns a shareable URL that opens the perspective in preview mode, so you (or a teammate) can have a sample conversation with it before deploying. Behavior: - Read-only. The same stable preview URL every time for a given perspective; the link does not expire. - Conversations started from this URL are preview conversations and do NOT count toward the workspace's quota. - Anyone with the link can start a preview conversation, even unauthenticated — treat as semi-public. When to use this tool: - After perspective_create or perspective_update, to manually verify the perspective's behavior before going live. - To share a preview link with a teammate for review. When NOT to use this tool: - For production deployment — use perspective_get_embed_options, which returns embed snippets and a public share link whose conversations count toward quota. - To inspect existing real conversations — use perspective_list_conversations / perspective_get_conversation. Typical flow: 1. perspective_create → design 2. perspective_get_preview_link → test 3. perspective_update → refine 4. perspective_get_embed_options → deploy

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• glim_github_search

  glim.sh

  Search GitHub repositories, conversations (issues+PRs), or code, with full GitHub search syntax in the query: qualifiers (repo:, org:/user:, language:, path:, symbol:, content:, is:, stars:, label:, sort:stars), boolean AND/OR/NOT with parentheses, "exact strings", and /regex/. kind='repos': MINIMAL distinctive keywords - the project/library name only ('rtk', 'react query'); every extra word must ALL match and buries the canonical repo - filter with qualifiers, not prose. kind='code': ONE literal code pattern as it appears in files ('useState('), an "exact string", a /regex/, or symbol:name to find definitions, across 2.8M+ public repos; narrow with repo:/language:/path:. Not supported in code search: license:, enterprise:, is:vendored, is:generated. kind='conversations': returns compact previews - use glim_github_get for full content; sort: REPLACES relevance ranking (words match anywhere incl. comments), omit it for best matches. Set repo='owner/name' to scope to one repository (works with any kind; with repos it routes to conversations). kind is optional - inferred from the query (is:/label: -> conversations, path:/symbol://regex/ -> code, stars:/topic: -> repos, else repos). Returns compact text by default; pass format='json' for full structured data.

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• search

  Melvea Local Honey Discovery

  Search the Melvea local honey directory by free-text query and return matching producers as a list of results (id, title, url). Designed for ChatGPT Deep Research and Company Knowledge. Use for any local-honey discovery query that names or implies a place; the tool parses place and varietal from the query. Returns an honest empty list when nothing matches — never fabricate. Pair with fetch to retrieve full producer detail.

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• get_traveler_context

  gondola

  Get the user's saved travel context to personalize recommendations. Returns the user's loyalty programs and elite tiers, home airport, preferred airlines and cabin, preferred hotel chains, typical trip patterns (business vs leisure, budgets, frequent destinations), and any preferences they've stated or that have been learned from past conversations. Call this once at the start of a travel or planning session and weigh it across hotel, flight, and car recommendations — it is the single best source of who this traveler is. Requires a Gondola account (API key). Returns: Formatted travel context, or instructions to build one.

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• get_knowledge_version

  Marmo UI MCP

  Returns the MCP knowledge version: gitSha, indexedAt, componentCount, patternCount, uptimeSeconds. Call this ONCE per session before generating UI code so you know how fresh the design-system data is. Cheap to call. If gitSha is "unknown" or indexedAt is far in the past, surface that to the user before relying on the data.

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• 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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• get_session_dossiers

  VaultCrux Memory Core

  Retrieve pre-synthesized per-session memory dossiers (typed: experience | fact | preference; with When/Involving/To-purpose metadata). Use for multi-session or preference-style questions where stitching across conversations is the bottleneck — the dossier already summarises each session's key events. Two modes: mode='search' with a query (BM25-ish ranking over summary+purpose, optional type_filter), or mode='list' returns the tenant's most-recent dossiers chronologically. Tenants without FEATURE_SESSION_DOSSIERS enabled return an empty list (no error).

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• get_workload_logs

  Control Plane

  Query workload logs from a GVC. Provide structured params (gvc, workload, container, location, filter) OR a raw LogQL `query` — a raw query REPLACES the structured params, so it must embed ALL labels itself. Available labels: gvc, workload, container, location, provider, replica, stream — replica and stream are only reachable via a raw query. `filter` is a literal substring match (|=), not regex; for regex use a raw query with |~. Cron workload? Get jobExecutions via list_deployments (with `location`), then re-query with a raw query scoping replica= plus the execution's time window — embed gvc/workload/location labels in the raw query. Returns structured JSON with timestamps, messages, and labels. Recommended reading before first use: get_cpln_skill("workload-troubleshooting") — the runbook for this tool family (read once per session).

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• recommend

  Seemor Restaurant Intelligence

  Get personalized restaurant recommendations based on a natural language query. Uses cuisine, occasion, ambiance, price, and dimensional analysis to find the best matches. Returns ranked results with relevance levels and match reasons in 3-8 seconds. Include a location in your query or provide the location parameter.

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• list_conversations

  AIOProductOS MCP

  List support-chat conversations in the inbox (open + snoozed by default; pass status='all' to include closed). Read-only; returns the matching conversations, empty when the inbox is clear. Optional product_id to scope to one product; open a full thread with get_conversation.

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• forget

  Domains

  Delete a previously stored memory by key. Use when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier. Pair with remember and recall.

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• forget

  Arcgis Lubbock

  Delete a previously stored memory by key. Use when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier. Pair with remember and recall.

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• forget

  Arcgis Tempe

  Delete a previously stored memory by key. Use when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier. Pair with remember and recall.

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• forget

  Data Kcmo

  Delete a previously stored memory by key. Use when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier. Pair with remember and recall.

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• forget

  Library Of Congress

  Delete a previously stored memory by key. Use when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier. Pair with remember and recall.

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• forget

  Rentcast

  Delete a previously stored memory by key. Use when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier. Pair with remember and recall.

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• get_user_profile

  anamnese

  PRIMARY TOOL - Call this at the START of every conversation to load comprehensive user context. Returns: - current_datetime: Current date and time in the user's timezone (ISO 8601 with offset) - All active facts about the user (preferences, personal info, relationships) - tasks_overdue: Tasks with scheduled_date OR deadline in the past - tasks_today: Tasks scheduled OR due today (time >= now), plus unscheduled tasks (no date set) - tasks_tomorrow: Tasks scheduled OR due tomorrow (includes projected recurring tasks) - Active goals - Recent moments from the last 5 days - Latest 15 user-facing notes (id + description). Use get_note to retrieve full content. - ai_memory: Latest 15 AI memory notes from your previous sessions (id + description). Use get_note to retrieve full content. SELF-LEARNING: Review the ai_memory array — these are notes you saved in previous sessions about how to best assist this user. Load relevant ones with get_note. Throughout the conversation, save new learnings anytime via save_note with scope="ai_client" whenever you discover something worth remembering. - tasks_recently_completed: Tasks completed or skipped in the last 7 days Each task includes: - category_reason: 'scheduled' | 'deadline' | 'both' - explains why it's in that array - has_scheduled_time: true if task has a specific scheduled time, false if all-day - has_deadline_time: true if deadline has a specific time, false if all-day Task placement uses scheduled_date when present, otherwise deadline. Each task appears in exactly one category. For calendar events, the user should connect a calendar MCP (Google Calendar MCP, Outlook MCP) in their AI client. Query those MCPs alongside Anamnese for a complete daily view. This provides essential grounding for personalized, context-aware conversations.

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• search_threads

  DialogBrain

  Find or list chat threads/conversations — by topic, participant, unread/unanswered status, or recency. Omit `query` to list threads by filter. For message content use search.messages; for files use search.files. `since` filters by recency and pairs with only_unread / only_unanswered.

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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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