mcp memory domain knowledge

Local 2025-08-31 23:24:46 0

Knowledge And Memory @yodakeisuke/mcp-memory-domain-knowledge

This MCP server provides persistent memory integration for chat applications by utilizing a local knowledge graph to remember user information across interactions.

forked https://github.com/modelcontextprotocol/servers/tree/main

A basic implementation of persistent memory using a local knowledge graph. This lets Claude remember information about the user across chats.

Core Concepts

Entities

Entities are the primary nodes in the knowledge graph. Each entity has: - A unique name (identifier) - An entity type (e.g., "person", "organization", "event") - A list of observations

Example:

{
  "name": "John_Smith",
  "entityType": "person",
  "observations": ["Speaks fluent Spanish"]
}

Relations

Relations define directed connections between entities. They are always stored in active voice and describe how entities interact or relate to each other.

Example:

{
  "from": "John_Smith",
  "to": "Anthropic",
  "relationType": "works_at"
}

Observations

Observations are discrete pieces of information about an entity. They are:

Stored as strings
Attached to specific entities
Can be added or removed independently
Should be atomic (one fact per observation)

Example:

{
  "entityName": "John_Smith",
  "observations": [
    "Speaks fluent Spanish",
    "Graduated in 2019",
    "Prefers morning meetings"
  ]
}

API

Tools

create_entities
Create multiple new entities in the knowledge graph
Input: entities (array of objects)
- Each object contains:
- name (string): Entity identifier
- entityType (string): Type classification
- observations (string[]): Associated observations
Ignores entities with existing names
create_relations
Create multiple new relations between entities
Input: relations (array of objects)
- Each object contains:
- from (string): Source entity name
- to (string): Target entity name
- relationType (string): Relationship type in active voice
Skips duplicate relations
add_observations
Add new observations to existing entities
Input: observations (array of objects)
- Each object contains:
- entityName (string): Target entity
- contents (string[]): New observations to add
Returns added observations per entity
Fails if entity doesn't exist
delete_entities
Remove entities and their relations
Input: entityNames (string[])
Cascading deletion of associated relations
Silent operation if entity doesn't exist
delete_observations
Remove specific observations from entities
Input: deletions (array of objects)
- Each object contains:
- entityName (string): Target entity
- observations (string[]): Observations to remove
Silent operation if observation doesn't exist
delete_relations
Remove specific relations from the graph
Input: relations (array of objects)
- Each object contains:
- from (string): Source entity name
- to (string): Target entity name
- relationType (string): Relationship type
Silent operation if relation doesn't exist
read_graph
Read the entire knowledge graph
No input required
Returns complete graph structure with all entities and relations
search_nodes
Search for nodes based on one or more keywords
Input: query (string)
- Space-separated keywords (e.g., "budget utility")
- Multiple keywords are treated as OR conditions
Searches across:
- Entity names
- Entity types
- Subdomains
- Observation content
Matching behavior:
- Case-insensitive
- Partial word matching
- Any keyword can match any field
- Returns entities matching ANY of the keywords
Returns matching entities and their relations
Example queries:
- Single keyword: "budget"
- Multiple keywords: "budget utility"
- With special chars: "budget & utility"
open_nodes
Retrieve specific nodes by name
Input: names (string[])
Returns:
- Requested entities
- Relations between requested entities
Silently skips non-existent nodes

Usage with Claude Desktop

Setup

Add this to your claude_desktop_config.json:

Docker

{
  "mcpServers": {
    "memory": {
      "command": "docker",
      "args": ["run", "-i", "--rm", "mcp/memory"]
    }
  }
}

NPX

{
  "mcpServers": {
    "memory": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-memory"
      ]
    }
  }
}

NPX with custom setting

The server can be configured using the following environment variables:

{
  "mcpServers": {
    "memory": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-memory"
      ],
      "env": {
        "MEMORY_FILE_PATH": "/path/to/custom/memory.json"
      }
    }
  }
}

MEMORY_FILE_PATH: Path to the memory storage JSON file (default: memory.json in the server directory)

System Prompt

The prompt for utilizing memory depends on the use case. Changing the prompt will help the model determine the frequency and types of memories created.

Here is an example prompt for chat personalization. You could use this prompt in the "Custom Instructions" field of a Claude.ai Project.

Follow these steps for each interaction:

1. User Identification:
   - You should assume that you are interacting with default_user
   - If you have not identified default_user, proactively try to do so.

2. Memory Retrieval:
   - Always begin your chat by saying only "Remembering..." and retrieve all relevant information from your knowledge graph
   - Always refer to your knowledge graph as your "memory"
   - When searching your memory, you can use multiple keywords to find related information
   - Example searches:
     * Single concept: "programming"
     * Related concepts: "programming python"
     * Specific domain with role: "work engineer"

3. Memory Creation:
   - While conversing with the user, be attentive to any new information that falls into these categories:
     a) Basic Identity (age, gender, location, job title, education level, etc.)
     b) Behaviors (interests, habits, etc.)
     c) Preferences (communication style, preferred language, etc.)
     d) Goals (goals, targets, aspirations, etc.)
     e) Relationships (personal and professional relationships up to 3 degrees of separation)
   - When storing information, use specific and descriptive keywords that will help in future searches

4. Memory Update:
   - If any new information was gathered during the interaction, update your memory as follows:
     a) Create entities for recurring organizations, people, and significant events
     b) Connect them to the current entities using relations
     c) Store facts about them as observations
     d) Use clear and searchable terms in entity names and observations to facilitate future retrieval

Building

Docker:

docker build -t mcp/memory -f src/memory/Dockerfile .

License

This MCP server is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please see the LICENSE file in the project repository.

[
  {
    "description": "Create multiple new entities in the knowledge graph",
    "inputSchema": {
      "properties": {
        "entities": {
          "items": {
            "properties": {
              "entityType": {
                "description": "The type of the entity",
                "type": "string"
              },
              "name": {
                "description": "The name of the entity",
                "type": "string"
              },
              "observations": {
                "description": "An array of observation contents associated with the entity",
                "items": {
                  "type": "string"
                },
                "type": "array"
              },
              "subdomain": {
                "description": "The loglass subdomain this knowledge belongs to (e.g., 'allocation', 'report', 'accounts', 'plans', 'actual' etc.). Can be omitted if the knowledge spans multiple domains.",
                "type": "string"
              }
            },
            "required": [
              "name",
              "entityType",
              "observations"
            ],
            "type": "object"
          },
          "type": "array"
        }
      },
      "required": [
        "entities"
      ],
      "type": "object"
    },
    "name": "create_entities"
  },
  {
    "description": "Create multiple new relations between entities in the knowledge graph. Relations should be in active voice",
    "inputSchema": {
      "properties": {
        "relations": {
          "items": {
            "properties": {
              "from": {
                "description": "The name of the entity where the relation starts",
                "type": "string"
              },
              "relationType": {
                "description": "The type of the relation",
                "type": "string"
              },
              "to": {
                "description": "The name of the entity where the relation ends",
                "type": "string"
              }
            },
            "required": [
              "from",
              "to",
              "relationType"
            ],
            "type": "object"
          },
          "type": "array"
        }
      },
      "required": [
        "relations"
      ],
      "type": "object"
    },
    "name": "create_relations"
  },
  {
    "description": "Add new observations to existing entities in the knowledge graph",
    "inputSchema": {
      "properties": {
        "observations": {
          "items": {
            "properties": {
              "contents": {
                "description": "An array of observation contents to add",
                "items": {
                  "type": "string"
                },
                "type": "array"
              },
              "entityName": {
                "description": "The name of the entity to add the observations to",
                "type": "string"
              }
            },
            "required": [
              "entityName",
              "contents"
            ],
            "type": "object"
          },
          "type": "array"
        }
      },
      "required": [
        "observations"
      ],
      "type": "object"
    },
    "name": "add_observations"
  },
  {
    "description": "Delete multiple entities and their associated relations from the knowledge graph",
    "inputSchema": {
      "properties": {
        "entityNames": {
          "description": "An array of entity names to delete",
          "items": {
            "type": "string"
          },
          "type": "array"
        }
      },
      "required": [
        "entityNames"
      ],
      "type": "object"
    },
    "name": "delete_entities"
  },
  {
    "description": "Delete specific observations from entities in the knowledge graph",
    "inputSchema": {
      "properties": {
        "deletions": {
          "items": {
            "properties": {
              "entityName": {
                "description": "The name of the entity containing the observations",
                "type": "string"
              },
              "observations": {
                "description": "An array of observations to delete",
                "items": {
                  "type": "string"
                },
                "type": "array"
              }
            },
            "required": [
              "entityName",
              "observations"
            ],
            "type": "object"
          },
          "type": "array"
        }
      },
      "required": [
        "deletions"
      ],
      "type": "object"
    },
    "name": "delete_observations"
  },
  {
    "description": "Delete multiple relations from the knowledge graph",
    "inputSchema": {
      "properties": {
        "relations": {
          "description": "An array of relations to delete",
          "items": {
            "properties": {
              "from": {
                "description": "The name of the entity where the relation starts",
                "type": "string"
              },
              "relationType": {
                "description": "The type of the relation",
                "type": "string"
              },
              "to": {
                "description": "The name of the entity where the relation ends",
                "type": "string"
              }
            },
            "required": [
              "from",
              "to",
              "relationType"
            ],
            "type": "object"
          },
          "type": "array"
        }
      },
      "required": [
        "relations"
      ],
      "type": "object"
    },
    "name": "delete_relations"
  },
  {
    "description": "Read the entire knowledge graph",
    "inputSchema": {
      "properties": {},
      "type": "object"
    },
    "name": "read_graph"
  },
  {
    "description": "Search for nodes in the knowledge graph based on one or more keywords. The search covers entity names, types, subdomains, and observation content. Multiple keywords are treated as OR conditions, where any keyword must match somewhere in the entity's fields.",
    "inputSchema": {
      "properties": {
        "query": {
          "description": "Space-separated keywords to match against entity fields. Any keyword must match (OR condition). Example: 'budget management' will find entities where either 'budget' or 'management' appears in any field.",
          "type": "string"
        }
      },
      "required": [
        "query"
      ],
      "type": "object"
    },
    "name": "search_nodes"
  },
  {
    "description": "Open specific nodes in the knowledge graph by their names. Returns the complete node information including subdomain and all metadata.",
    "inputSchema": {
      "properties": {
        "names": {
          "description": "An array of entity names to retrieve, returning full entity information including subdomain",
          "items": {
            "type": "string"
          },
          "type": "array"
        }
      },
      "required": [
        "names"
      ],
      "type": "object"
    },
    "name": "open_nodes"
  }
]