{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"memory-mcp-server","slug":"memory-mcp-server","name":"Memory MCP Server","type":"mcp","url":"https://github.com/modelcontextprotocol/servers/tree/main/src/memory","page_url":"https://unfragile.ai/memory-mcp-server","categories":["mcp-servers"],"tags":["memory","knowledge-graph","official","reference"],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"memory-mcp-server__cap_0","uri":"capability://memory.knowledge.entity.relationship.graph.persistence.with.json.file.backend","name":"entity-relationship graph persistence with json file backend","description":"Stores and retrieves entities, relations, and observations in a local JSON file using a graph-based data model. The server implements MCP Tools that allow clients to create entities (with properties), define typed relationships between entities, and record observations tied to entities. Data persists across conversation sessions in a single JSON file, enabling stateful knowledge accumulation without requiring external databases or network calls.","intents":["I want my LLM agent to remember facts about users, projects, or entities across multiple conversations","I need to build a knowledge graph that the LLM can query and update incrementally","I want to store structured relationships (e.g., 'user X works on project Y') that persist between sessions"],"best_for":["LLM application developers building conversational agents with long-term memory","Teams prototyping knowledge-graph-backed chatbots without infrastructure overhead","Solo developers building personal AI assistants that need to remember context"],"limitations":["No built-in query optimization — full JSON file is loaded into memory on each operation, causing O(n) performance degradation with large graphs (>10k entities)","Single-file storage model means no concurrent write safety — multiple MCP clients writing simultaneously may corrupt data","No schema validation or type enforcement — relationships and properties are stored as-is without validation against a predefined schema","No built-in versioning or audit trail — overwrites are permanent with no history tracking"],"requires":["Node.js 16+ (TypeScript SDK requirement)","MCP client implementation (Claude, custom LLM application, or MCP inspector)","Write permissions to the directory where JSON file will be stored","Sufficient disk space for JSON serialization of graph (typically <1MB for small-to-medium graphs)"],"input_types":["entity names (strings)","entity properties (key-value objects with string/number/boolean values)","relationship types (strings)","observation text (strings)","entity IDs (strings)"],"output_types":["structured JSON representing entities with properties","relationship lists with source, target, and relation type","observation records with timestamps and associated entities","query results as filtered entity/relationship subsets"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"memory-mcp-server__cap_1","uri":"capability://tool.use.integration.mcp.tool.interface.for.memory.operations","name":"mcp tool interface for memory operations","description":"Exposes memory graph operations as MCP Tools (function-calling interface) that LLM clients can invoke. The server implements Tools following the MCP protocol specification, including tool definitions with JSON schemas, input validation, and structured responses. Each Tool maps to a specific memory operation (create entity, add relation, record observation, query entities) and returns results in a format the LLM can parse and reason about.","intents":["I want my LLM to be able to call functions that modify or query the memory graph during conversation","I need the LLM to understand what memory operations are available and their parameters via schema introspection","I want structured, predictable responses from memory operations that the LLM can reliably parse"],"best_for":["LLM application developers using MCP-compatible clients (Claude, custom agents)","Teams building agentic systems where the LLM needs to explicitly manage state","Developers learning MCP Tool patterns through reference implementation"],"limitations":["Tool invocation is synchronous — no streaming or async operation support, so large graph queries block the LLM response","No built-in rate limiting or quota enforcement — LLM can spam memory operations without throttling","Tool schemas are static — no dynamic schema generation based on current graph structure or entity types","Error handling is basic — malformed requests return generic error messages without detailed validation feedback"],"requires":["MCP client that supports Tool calling (Claude API with tool_use, custom MCP client implementation)","Understanding of JSON schema format for Tool parameter definitions","MCP server transport configured (stdio, HTTP, or SSE)"],"input_types":["tool name (string)","tool parameters as JSON object matching schema"],"output_types":["tool result as JSON object","error response with error code and message"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"memory-mcp-server__cap_2","uri":"capability://memory.knowledge.entity.creation.and.property.management","name":"entity creation and property management","description":"Provides a Tool that allows clients to create new entities in the knowledge graph with arbitrary key-value properties. The implementation stores entities with unique IDs, names, and a properties object. Properties can be strings, numbers, or booleans. The Tool validates that entity names are non-empty and returns the created entity with its assigned ID, enabling the LLM to reference the entity in subsequent operations.","intents":["I want to create a new entity (e.g., a user, project, or concept) and store properties about it","I need to assign a unique ID to each entity so I can reference it in relationships","I want to update or add properties to an existing entity incrementally"],"best_for":["Conversational agents that need to onboard new entities (users, topics, objects) during conversation","Knowledge base builders that incrementally populate a graph from LLM reasoning","Multi-turn conversations where entities are discovered and refined over time"],"limitations":["No schema enforcement — any property key-value pairs are accepted, risking inconsistent entity structures","No property type validation beyond basic JSON types — complex nested objects are not supported","No duplicate detection — creating an entity with the same name twice results in two separate entities","Properties are immutable after creation — updating properties requires re-creating the entity or using a separate update operation"],"requires":["Entity name (non-empty string)","Optional properties object (key-value pairs with string/number/boolean values)"],"input_types":["entity name (string)","properties (JSON object with string/number/boolean values)"],"output_types":["created entity object with id, name, and properties"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"memory-mcp-server__cap_3","uri":"capability://memory.knowledge.typed.relationship.creation.and.querying","name":"typed relationship creation and querying","description":"Provides Tools to create directed relationships between entities with a specified relation type (e.g., 'works_on', 'knows', 'parent_of'). Relationships are stored as source entity ID, target entity ID, and relation type string. The implementation allows querying relationships by source entity, target entity, or relation type, returning matching relationships. This enables the LLM to express and retrieve semantic connections between entities.","intents":["I want to express that entity A has a specific relationship to entity B (e.g., 'user X works on project Y')","I need to query all relationships of a certain type to understand the graph structure","I want to find all entities related to a given entity through a specific relationship type"],"best_for":["Knowledge graph applications where semantic relationships are central (social networks, organizational hierarchies, project assignments)","LLM agents that need to reason about connections between entities","Systems where relationship types are predefined or discovered dynamically"],"limitations":["No relationship properties — relationships are only source, target, and type; no metadata like timestamps or confidence scores","No bidirectional query optimization — querying 'all entities that have relationship X to entity Y' requires scanning all relationships","No relationship constraints — the system allows creating relationships between non-existent entities without validation","No cycle detection — the system allows creating circular relationship chains without warning"],"requires":["Source entity ID (must exist in graph)","Target entity ID (must exist in graph)","Relation type (string, e.g., 'works_on', 'knows')"],"input_types":["source entity ID (string)","target entity ID (string)","relation type (string)"],"output_types":["created relationship object with source, target, and relation type","query results as array of matching relationships"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"memory-mcp-server__cap_4","uri":"capability://memory.knowledge.observation.recording.with.temporal.context","name":"observation recording with temporal context","description":"Provides a Tool to record observations (facts or events) associated with one or more entities, with automatic timestamp recording. Observations are stored as text content, associated entity IDs, and a creation timestamp. The implementation allows querying observations by entity, enabling the LLM to retrieve historical facts about entities. This enables the system to track events, notes, or discoveries over time.","intents":["I want to record a fact or event about an entity with a timestamp for later reference","I need to retrieve all observations about a specific entity to understand its history","I want the system to automatically timestamp observations so I can reason about temporal ordering"],"best_for":["Conversational agents that need to log discoveries or user statements for future reference","Systems tracking entity history or audit trails","LLM applications that reason about temporal sequences of events"],"limitations":["Timestamps are server-side only — no client control over observation time, limiting backdating or correction","No observation editing — once recorded, observations cannot be modified or deleted","No observation metadata — only text, entities, and timestamp; no confidence scores, sources, or tags","Temporal queries are limited — no range queries or time-based filtering, only retrieval of all observations for an entity"],"requires":["Observation text (non-empty string)","Associated entity IDs (array of strings, at least one)"],"input_types":["observation text (string)","entity IDs (array of strings)"],"output_types":["recorded observation object with text, entity IDs, and timestamp","query results as array of observations for a given entity"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"memory-mcp-server__cap_5","uri":"capability://memory.knowledge.graph.querying.and.entity.retrieval","name":"graph querying and entity retrieval","description":"Provides Tools to query the knowledge graph and retrieve entities, relationships, and observations. The implementation supports querying by entity ID, entity name (substring or exact match), relationship type, or observation content. Results are returned as structured JSON arrays, allowing the LLM to inspect the graph state and make decisions based on current knowledge. Queries are executed by filtering the in-memory graph representation.","intents":["I want to retrieve all entities matching a name pattern to find relevant entities in the graph","I need to check if an entity already exists before creating a duplicate","I want to inspect all relationships of a certain type to understand the graph structure","I need to retrieve observations about an entity to provide context to the LLM"],"best_for":["LLM agents that need to inspect graph state before making decisions","Conversational systems that check for existing entities to avoid duplication","Knowledge base applications where the LLM needs to reason about graph structure"],"limitations":["No full-text search — queries are simple substring or exact matches, not semantic search","No query optimization — all queries scan the entire in-memory graph, causing O(n) performance","No pagination — large result sets are returned in full, potentially overwhelming the LLM context","No complex queries — no support for multi-hop traversals, aggregations, or graph pattern matching"],"requires":["Query parameters (entity ID, name pattern, relationship type, or observation text)"],"input_types":["entity ID (string)","entity name pattern (string)","relationship type (string)","observation text pattern (string)"],"output_types":["array of matching entities with properties","array of matching relationships","array of matching observations"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"memory-mcp-server__cap_6","uri":"capability://tool.use.integration.mcp.server.lifecycle.and.transport.management","name":"mcp server lifecycle and transport management","description":"Implements the MCP server lifecycle using the TypeScript SDK, including initialization, Tool registration, request handling, and graceful shutdown. The server supports standard MCP transports (stdio, HTTP, SSE) through SDK abstractions. On startup, the server loads the JSON memory file (or creates it if missing), registers all memory Tools with the MCP protocol, and begins accepting requests from MCP clients. The implementation handles connection lifecycle events and ensures the JSON file is persisted after each operation.","intents":["I want to run a memory server that MCP clients can connect to and use","I need the server to persist memory state to disk so it survives restarts","I want to integrate this memory server into my MCP client configuration"],"best_for":["Developers integrating the memory server into MCP client configurations (Claude, custom agents)","Teams deploying MCP servers in containerized or serverless environments","Reference implementation learners studying MCP server patterns"],"limitations":["No built-in authentication or authorization — any MCP client with access to the server can read/write all memory","No transaction support — concurrent writes from multiple clients may result in data loss or corruption","File I/O is synchronous — large JSON files cause blocking on read/write operations","No backup or recovery mechanism — data loss is permanent if the JSON file is deleted or corrupted"],"requires":["Node.js 16+ runtime","MCP TypeScript SDK (installed via npm)","Write permissions to the directory where memory JSON file will be stored","MCP client configured to connect to the server (via stdio, HTTP, or SSE)"],"input_types":["MCP protocol messages (JSON-RPC)"],"output_types":["MCP protocol responses (JSON-RPC)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"memory-mcp-server__cap_7","uri":"capability://memory.knowledge.json.file.based.persistence.with.atomic.writes","name":"json file-based persistence with atomic writes","description":"Persists the entire knowledge graph to a single JSON file on disk after each operation. The implementation reads the JSON file on server startup, maintains the graph in memory, and writes the entire graph back to the file after create/update operations. The file format is a JSON object containing arrays of entities, relationships, and observations. This approach ensures data survives server restarts but requires the entire graph to be serialized on each write.","intents":["I want memory to persist across server restarts without setting up a database","I need to inspect the memory graph as human-readable JSON for debugging","I want to manually edit or backup the memory file if needed"],"best_for":["Development and prototyping where database setup overhead is undesirable","Single-user or low-concurrency scenarios where atomic writes are sufficient","Deployments where external dependencies (databases) are not available"],"limitations":["No concurrent write safety — multiple processes writing simultaneously will corrupt the file; requires external locking","Write amplification — every operation rewrites the entire graph, causing O(n) I/O even for single-entity updates","No incremental backups — each write is a full snapshot, wasting storage for large graphs","File size limits — JSON serialization overhead grows with graph size, eventually hitting filesystem or memory limits","No transaction rollback — failed writes leave the file in an inconsistent state with no recovery mechanism"],"requires":["Writable filesystem with sufficient space for JSON serialization","File path specified in server configuration (default: memory.json in current directory)","No other processes writing to the same file simultaneously"],"input_types":["knowledge graph in-memory representation (entities, relationships, observations)"],"output_types":["JSON file on disk with structure: { entities: [...], relationships: [...], observations: [...] }"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"memory-mcp-server__cap_8","uri":"capability://memory.knowledge.llm.friendly.graph.representation.and.reasoning","name":"llm-friendly graph representation and reasoning","description":"Structures the knowledge graph in a way that LLMs can easily understand and reason about: entities with properties, typed relationships, and timestamped observations. The Tool responses return structured JSON that the LLM can parse and reason about. The design avoids complex graph theory concepts (like graph traversal algorithms or cycle detection) in favor of simple, explicit data structures. This enables the LLM to make decisions about memory operations without requiring specialized graph reasoning capabilities.","intents":["I want the LLM to understand the memory graph structure without needing to learn graph theory","I need the LLM to reason about entities, relationships, and observations in natural language","I want the LLM to make decisions about memory operations based on simple, explicit data"],"best_for":["LLM application developers who want to add memory without specialized knowledge","Conversational agents that need to reason about entities and relationships naturally","Teams building LLM systems where the model should understand memory operations intuitively"],"limitations":["No implicit reasoning — the LLM must explicitly query the graph to understand structure; no automatic inference or rule engines","No graph visualization — the system returns JSON, not visual representations, making large graphs hard to understand","Limited expressiveness — the simple model cannot represent complex relationships like n-ary relations or relationship properties","No semantic understanding — the system treats relationship types as arbitrary strings, not understanding their meaning"],"requires":["LLM capable of understanding and generating JSON","LLM with sufficient context window to reason about graph queries and results"],"input_types":["natural language instructions from the LLM"],"output_types":["structured JSON representing entities, relationships, and observations"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"memory-mcp-server__headline","uri":"capability://memory.knowledge.persistent.knowledge.graph.memory.server","name":"persistent knowledge graph memory server","description":"An official MCP server designed for maintaining long-term memory in large language models by storing entities, relations, and observations in a local JSON file, enabling seamless conversation continuity.","intents":["best memory server for LLMs","persistent memory solution for AI","MCP server for knowledge graphs","how to implement long-term memory in AI","memory management for conversational agents"],"best_for":["developers building AI with memory capabilities"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":72,"verified":false,"data_access_risk":"high","permissions":["Node.js 16+ (TypeScript SDK requirement)","MCP client implementation (Claude, custom LLM application, or MCP inspector)","Write permissions to the directory where JSON file will be stored","Sufficient disk space for JSON serialization of graph (typically <1MB for small-to-medium graphs)","MCP client that supports Tool calling (Claude API with tool_use, custom MCP client implementation)","Understanding of JSON schema format for Tool parameter definitions","MCP server transport configured 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current graph structure or entity types","Error handling is basic — malformed requests return generic error messages without detailed validation feedback","No schema enforcement — any property key-value pairs are accepted, risking inconsistent entity structures","No property type validation beyond basic JSON types — complex nested objects are not supported","builder identity is not verified yet","no observed match outcomes 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