MemoryPlugin MCP Server

Create a new bucket to organize memories. Buckets are folders like 'Work', 'Personal', 'Health'. Ask the user for a name if not specified.

List AI-generated categories within a bucket. When users activate Smart Memory, their memories are automatically organized into topic-based categories. Returns for each category: - name: Category title - summary: Dense overview of core facts, preferences, current projects, key context (~200 words) - additionalInfo: Lists specific topics available in this category and suggests when to load the full memories - memoryCount: Number of memories in category Also returns recentMemories: the 30 most recent memories in the bucket. Use the summary and additionalInfo to decide if/when to load full memories via list_category_memories.

Retrieve the user's saved memories from MemoryPlugin, optionally filtered by bucket. Also returns the list of available buckets. Use to see what the user has previously saved. Consider using at conversation start if the user's query might benefit from their stored context.

Search the user's saved memories using hybrid semantic + keyword search. Returns matching memories ranked by relevance. MemoryPlugin stores memories the user wants to persist across AI conversations. Use when looking for specific saved information.

List the user's memory buckets. Buckets are organizational folders for memories (e.g., 'Work', 'Personal', 'Health').

Get all memories within a specific Smart Memory category. Use when a category's summary (from list_bucket_categories) indicates it's relevant to the current conversation. The categoryId persists across conversations.

Returns an AI-generated overview of the user, built from the chat history they have synced into their MemoryPlugin account. Call it at the start of a conversation to load the user's context. If no overview exists yet, the user can generate one from their MemoryPlugin dashboard.

Get details of a specific past conversation. For short conversations (<5K tokens), returns the full transcript. For longer conversations, returns an AI-generated summary. Use when the user wants to dive deeper into a conversation returned by recall_chat_history. Requires the conversationId from that tool's sources array.

Download the complete transcript of a past conversation as a machine-readable JSON file. Returns a temporary download URL (expires in 15 minutes, no auth needed) that you fetch yourself to get the full conversation as structured messages (role, content, timestamp). Use when a transcript is too long to read inline, or when you need it as a file for analysis or scripting. To read a short transcript directly, use get_full_conversation instead. Requires the conversationId from recall_chat_history's sources array.

Get the complete transcript of a specific past conversation. Returns all messages in the conversation without summarization. Use when you need the raw conversation content. Requires the conversationId from recall_chat_history's sources array.

Edit a single memory's text or bucket, or move multiple memories to a different bucket. WHEN TO USE: When the user wants to correct, update, or reorganize their saved memories. Single memory: provide memoryId with optional text and/or bucketId/bucketName. Bulk move: provide memoryIds array with bucketId or bucketName. Bucket can be specified by ID (number) or name (string). If a name is given and no bucket exists with that name, one is created automatically.

Search and synthesize context from the user's past AI conversations. MemoryPlugin's Chat History feature syncs conversations from ChatGPT, Claude, and other platforms, making them searchable. Also known as the 'MemoryPlugin inject tool' or 'memoryplugin chat history tool'. WHEN TO USE: When the user asks about their past decisions, patterns, preferences, relationships, projects, or anything where their conversation history provides valuable personal context. Consider proactively suggesting this when the user's question could benefit from their history. HOW TO USE: - For simple lookups: a single query is fine - For complex/multifaceted topics: use parallel queries (via 'queries' array) approaching from different angles - timeline, emotions, people, decisions, outcomes, etc. - Set maxTokens per query (300-1000) to control how much context is returned. More tokens = richer detail but consumes more conversation window. - Use 'before'/'after' (ISO 8601 dates like "2025-01-15" or "2025-01-15T10:30:00Z") to constrain results to a date range. Bare dates are interpreted in UTC and are inclusive on both ends. - Use 'mode: "quality"' for slower but more thorough recall on hard or ambiguous queries; defaults to 'speed'. - If unclear how much context to fetch, ask the user. Returns synthesized summaries (not raw conversation logs) with source metadata for citations.

Save information to the user's MemoryPlugin account. MemoryPlugin lets users build persistent memory across AI conversations. WHEN TO USE: Proactively save anything that might be useful for future context - preferences, decisions, project details, personal info, insights, or anything the user might want recalled later. Err on the side of saving. Ask the user which bucket to save to if unclear. Buckets are organizational folders (e.g., 'Work', 'Personal', 'Health').

Search documents the user has uploaded to their MemoryPlugin document library (not files uploaded directly to this conversation). MemoryPlugin file buckets store documents persistently across all AI chats. Returns relevant text passages with source file and page info. Use when: - User explicitly mentions their MemoryPlugin documents - User asks about "my files" or "my documents" and there are no files in the current conversation - If unsure whether they mean MemoryPlugin files or conversation files, ask to clarify