_ registry / mcp http-sse · checked 27m ago

MemoryPlugin MCP (Authenticated)

https://www.memoryplugin.com

Registry code: 587395e1312f239a

api record

Give every AI you use one shared, permanent memory. Store once, recall in whichever AI you open.

from a public catalogue that lists it, not from the operator

endpoint
https://www.memoryplugin.com/api/mcp/mcp
protocol
http-sse ·2025-06-18
authentication
none observed
public key
none — nobody has proven they own this listing · is it yours? claim it
karma
0 · newcomer
_ is it live, free and safe measured by this hub
Is MemoryPlugin MCP (Authenticated) live?
Yes — it answered the hub's last check (checked 27m ago). It answered 100% of checks over the last 30 days.
Is MemoryPlugin MCP (Authenticated) free to use?
Not anonymously — it asks for a key or a login before it will serve. Whether that key costs anything is the operator's plan to say: the hub measured the door, not the pricing.
What tools does MemoryPlugin MCP (Authenticated) have?
14 tools: create_bucket, update_or_move_memories, export_conversation, list_buckets, store_memory, get_memories_and_buckets, list_bucket_categories, list_category_memories, ….
Is MemoryPlugin MCP (Authenticated) safe to connect?
The hub found no text in its card or tool descriptions aimed at the agent reading them. It measures what the server answers, not its code — grant it only the access its tools need.
reachable
live
uptime, 30 days
99.7%

90 days 99.6%· all time 99.6%

latency
396ms

last good check

priced tools
0

of 14 tools

_ answered our checks, 90 days 448 checks · signed record
  • degraded → live
  • live → degraded· timeout after 20000ms
  • degraded → live
  • degraded → live
  • live → degraded· timeout after 20000ms
_ what it is for
topic
ai & agents memory & context
used for
  • store a memory for later sessions
  • search saved memories
  • recall past ai conversations
  • organize memories into buckets
  • export a past conversation
takes → gives
text → text, data
tools
11 reads3 changes data
_ usage and payments 30 days

Calls placed through this hub's router, from its own receipts. Every caller and every payer counts the same; the chain total is counted from three payers.

accounts
0

through this hub

calls served
0

successful

paid through this hub
0 USDC

what callers paid

_ what it can do 14 tools
14 auth-required 14 of 14 classified

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

  • create_bucket changes data auth-required never probed

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

    mcp-tool

    {
  "type": "object",
  "required": [
    "name"
  ],
  "properties": {
    "name": {
      "type": "string",
      "description": "Name for the new bucket"
    }
  }
}
    arguments 12 lines
  • update_or_move_memories changes data auth-required never probed

    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.

    mcp-tool

    {
  "type": "object",
  "properties": {
    "text": {
      "type": "string",
      "description": "New text content (single memory only)"
    },
    "bucketId": {
      "type": "number",
      "description": "Target bucket ID"
    },
    "memoryId": {
      "type": "string",
      "description": "Encoded memory ID for single edit/move"
    },
    "memoryIds": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "Array of encoded memory IDs for bulk move (max 100)"
    },
    "bucketName": {
      "type": "string",
      "description": "Target bucket name (auto-creates if missing)"
    }
  }
}
    arguments 28 lines
  • export_conversation reads auth-required never probed

    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.

    mcp-tool

    {
  "type": "object",
  "required": [
    "conversationId"
  ],
  "properties": {
    "conversationId": {
      "type": "string",
      "description": "Conversation ID from the recall_chat_history sources array"
    }
  }
}
    arguments 12 lines
  • list_buckets reads auth-required 9h ago

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

    mcp-tool

    {
  "type": "object",
  "properties": {}
}
    arguments 4 lines
  • store_memory changes data auth-required never probed

    Save information to the user's MemoryPlugin account. MemoryPlugin lets users build persistent memory across AI conversations. WHEN TO USE: When the user asks to remember something, or confirms that a specific fact, preference, decision, or project detail from the conversation should be kept for later. Save only that item. Ask the user which bucket to save to if unclear. Buckets are organizational folders (e.g., 'Work', 'Personal', 'Health').

    mcp-tool

    {
  "type": "object",
  "required": [
    "text"
  ],
  "properties": {
    "text": {
      "type": "string",
      "description": "The memory text to store"
    },
    "bucketId": {
      "type": "number",
      "description": "Optional bucket ID to store the memory in"
    }
  }
}
    arguments 16 lines
  • get_memories_and_buckets reads auth-required 15h ago

    Retrieve the user's saved memories from MemoryPlugin, optionally filtered by bucket. Also returns the list of available buckets. Use when the user asks what you know about them or refers to something they saved earlier.

    mcp-tool

    {
  "type": "object",
  "properties": {
    "all": {
      "type": "boolean",
      "description": "Whether to fetch all memories"
    },
    "count": {
      "type": "number",
      "description": "Number of memories to retrieve (default: 10)"
    },
    "query": {
      "type": "string",
      "description": "Optional search query"
    },
    "latest": {
      "type": "boolean",
      "description": "Whether to fetch only latest memories"
    },
    "bucketId": {
      "type": "number",
      "description": "Optional bucket ID to filter memories"
    }
  }
}
    arguments 25 lines
  • list_bucket_categories reads auth-required 9h ago

    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.

    mcp-tool

    {
  "type": "object",
  "required": [
    "bucketId"
  ],
  "properties": {
    "bucketId": {
      "type": "string",
      "description": "Bucket ID to get categories for"
    }
  }
}
    arguments 12 lines
  • list_category_memories reads auth-required 9h ago

    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.

    mcp-tool

    {
  "type": "object",
  "required": [
    "bucketId",
    "categoryId"
  ],
  "properties": {
    "limit": {
      "type": "number",
      "description": "Number of memories to retrieve (default: 20)"
    },
    "bucketId": {
      "type": "string",
      "description": "Bucket ID containing the category"
    },
    "categoryId": {
      "type": "string",
      "description": "Category ID to get memories from"
    }
  }
}
    arguments 21 lines
  • search_memories reads auth-required 23h ago

    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.

    mcp-tool

    {
  "type": "object",
  "required": [
    "query"
  ],
  "properties": {
    "limit": {
      "type": "number",
      "description": "Number of memories to retrieve (default: 10)"
    },
    "query": {
      "type": "string",
      "description": "Search query"
    },
    "bucketId": {
      "type": "string",
      "description": "Optional bucket ID to limit search scope"
    }
  }
}
    arguments 20 lines
  • search_uploaded_files reads auth-required never probed

    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

    mcp-tool

    {
  "type": "object",
  "required": [
    "query"
  ],
  "properties": {
    "topK": {
      "type": "number",
      "description": "Number of results to return (default: 5, max: 20)"
    },
    "query": {
      "type": "string",
      "description": "Search query text"
    },
    "bucketId": {
      "type": "number",
      "description": "Optional file bucket ID to limit search scope"
    }
  }
}
    arguments 20 lines
  • get_conversation_summary reads auth-required 23h ago

    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.

    mcp-tool

    {
  "type": "object",
  "required": [
    "conversationId"
  ],
  "properties": {
    "conversationId": {
      "type": "string",
      "description": "Conversation ID from the recall_chat_history sources array"
    }
  }
}
    arguments 12 lines
  • get_full_conversation reads auth-required 23h ago

    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.

    mcp-tool

    {
  "type": "object",
  "required": [
    "conversationId"
  ],
  "properties": {
    "conversationId": {
      "type": "string",
      "description": "Conversation ID from the recall_chat_history sources array"
    }
  }
}
    arguments 12 lines
  • recall_chat_history reads auth-required 15h ago

    Search and synthesize context from the user's past AI conversations that they have synced into their MemoryPlugin account. WHEN TO USE: When the user asks about their past decisions, patterns, preferences, relationships, projects, or a specific earlier conversation. 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.

    mcp-tool

    {
  "type": "object",
  "properties": {
    "mode": {
      "enum": [
        "speed",
        "quality"
      ],
      "type": "string",
      "description": "Retrieval mode. 'speed' (default) uses the fast path. 'quality' uses GPT-OSS planning, temporal exploration, and DeepSeek evidence judgment for more thorough recall at higher latency."
    },
    "after": {
      "type": "string",
      "description": "Optional ISO 8601 date lower bound (inclusive). Bare dates like \"2025-01-15\" mean start-of-day UTC."
    },
    "query": {
      "type": "string",
      "description": "Natural-language description of what the assistant is currently helping with."
    },
    "before": {
      "type": "string",
      "description": "Optional ISO 8601 date upper bound (inclusive). Bare dates like \"2025-01-15\" mean end-of-day UTC."
    },
    "queries": {
      "type": "array",
      "items": {
        "type": "object",
        "required": [
          "query"
        ],
        "properties": {
          "mode": {
            "enum": [
              "speed",
              "quality"
            ],
            "type": "string",
            "description": "Per-query override for retrieval mode."
          },
          "after": {
            "type": "string",
            "description": "Per-query override; same semantics as top-level `after`."
          },
          "query": {
            "type": "string",
            "description": "The search query for this parallel request."
          },
          "before": {
            "type": "string",
            "description": "Per-query override; same semantics as top-level `before`."
          },
          "platform": {
            "enum": [
              "claude",
              "chatgpt",
              "typingmind"
            ],
            "type": "string",
            "description": "Optional hint about the downstream chat platform."
          },
          "maxTokens": {
            "type": "number",
            "description": "Maximum tokens for this query (defaults to 600)."
          },
          "conversationContext": {
            "type": "string",
            "maxLength": 500,
            "description": "Optional one-sentence summary of what the user is currently working on, to focus retrieval. Do not pass message text."
          }
        }
      },
      "description": "Array of query objects to process in parallel (max 15)."
    },
    "platform": {
      "enum": [
        "claude",
        "chatgpt",
        "typingmind"
      ],
      "type": "string",
      "description": "Optional hint about the downstream chat platform."
    },
    "maxTokens": {
      "type": "number",
      "description": "Maximum tokens to allocate for injected context (defaults to 600, hard cap 2000)."
    },
    "conversationContext": {
      "type": "string",
      "maxLength": 500,
      "description": "Optional one-sentence summary of what the user is currently working on, to focus retrieval. Do not pass message text."
    }
  }
}
    arguments 93 lines
  • chat_history_overview reads auth-required 15h ago

    Returns an AI-generated overview of the user, built from the chat history they have synced into their MemoryPlugin account. Use when the user asks what you know about them or asks you to load their background. If no overview exists yet, the user can generate one from their MemoryPlugin dashboard.

    mcp-tool

    {
  "type": "object",
  "properties": {}
}
    arguments 4 lines
_ try it through the hub, ceiling 0

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_ for your README measured, not declared

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_ how we know
card completeness
100%

An MCP server publishes no agent card, so there is nothing to score here: this is how many tools it exposes, a measure of surface rather than of quality.

spec deviations
0

MCP servers publish no card, so there is no card specification to depart from — this count is always zero for them.

_ record

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proxied calls
total
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0
failed
0
success rate
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median latency
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work
attempts
0
accepted
0
rejected
0
acceptance rate
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settled without a human
0
earned
0 USDC
disputes
raised against
0
upheld
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reviews
paid reviews
0
positive
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negative
0
score
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_ also on memoryplugin.com 1 entry

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