[{"_1":2,"_747":-5,"_748":-5},"loaderData",{"_3":4,"_20":21,"_22":23,"_24":25,"_527":528},"root",{"_5":6,"_7":8},"toasts",[],"visitor",{"_9":10,"_11":12},"viewportWidth",1280,"visitorSession",{"_13":14,"_16":-5,"_17":18,"_19":-5},"attributes",[15],"BOT","membership","uid","dZ6ZPCyUNM","userAccount","routes/_public/_layout",{},"routes/_public/mcp/servers/~namespace/_layout",{},"routes/_public/mcp/servers/~namespace/~slug/_pages/_layout",{"_26":-5,"_27":28,"_29":30,"_446":447},"githubUser","maintainer",false,"mcpServer",{"_31":32,"_33":34,"_13":64,"_73":74,"_75":74,"_76":77,"_82":83,"_104":-5,"_105":106,"_107":108,"_109":110,"_92":127,"_128":97,"_129":-5,"_130":-5,"_131":132,"_133":134,"_141":-5,"_142":143,"_149":-5,"_117":93,"_150":-5,"_151":152,"_17":443,"_444":445},"addedAt",["D",1741480651197],"argumentsJsonSchema",{"_35":36,"_61":62,"_43":63},"properties",{"_37":38,"_45":46,"_48":49,"_51":52,"_54":55,"_58":59},"ES_NODE",{"_39":40,"_41":42,"_43":44},"default","http://localhost:9200","description","Elasticsearch node URL","type","string","ES_PASSWORD",{"_41":47,"_43":44},"Elasticsearch password (if authentication is enabled)","ES_USERNAME",{"_41":50,"_43":44},"Elasticsearch username (if authentication is enabled)","KG_DEFAULT_ZONE",{"_39":39,"_41":53,"_43":44},"Default memory zone to use","KG_INDEX_PREFIX",{"_39":56,"_41":57,"_43":44},"knowledge-graph","Prefix for Elasticsearch indices","MEMORY_FILE_PATH",{"_41":60,"_43":44},"Path to memory JSON file (for import/export)","required",[],"object",[65,66,67,68,69,70,71,72],"category:knowledge-and-memory","category:databases","category:search","hosting:hybrid","language:typescript","security_grade:a","quality_grade:a","license_grade:f","descriptionMarkdown","Provides a scalable knowledge graph implementation for Model Context Protocol using Elasticsearch, enabling AI models to store and query information with advanced search capabilities, memory-like behavior, and multi-zone architecture.","descriptionPlainText","githubProject",{"_78":79,"_80":81},"readme","# MCP Memory: Persistent Memory for AI Conversations 🧠\n\n![Version](https://img.shields.io/badge/version-1.0.0-blue)\n![License](https://img.shields.io/badge/license-MIT-green)\n![Elasticsearch](https://img.shields.io/badge/Elasticsearch-7.x-yellow)\n![Node](https://img.shields.io/badge/node-18+-green)\n\n> **Give your AI a memory that persists across conversations.** Never lose important context again.\n\nMCP Memory is a robust, Elasticsearch-backed knowledge graph system that gives AI models persistent memory beyond the limits of their context windows. Built for the Model Context Protocol (MCP), it ensures your LLMs remember important information forever, creating more coherent, personalized, and effective AI conversations.\n\n

\n $\"MCP$ \n

\n\n## 🌟 Why AI Models Need Persistent Memory\n\nEver experienced these frustrations with AI assistants?\n\n- Your AI forgetting crucial details from earlier conversations\n- Having to repeat the same context every time you start a new chat\n- Losing valuable insights once the conversation history fills up\n- Inability to reference past work or decisions\n\nMCP Memory solves these problems by creating a structured, searchable memory store that preserves context indefinitely. Your AI can now build meaningful, long-term relationships with users and maintain coherence across days, weeks, or months of interactions.\n\n## ✨ Key Features\n\n- **📊 Persistent Memory**: Store and retrieve information across multiple sessions\n- **🔍 Smart Search**: Find exactly what you need with powerful Elasticsearch queries\n- **📓 Contextual Recall**: AI automatically prioritizes relevant information based on the conversation\n- **🧩 Relational Understanding**: Connect concepts with relationships that mimic human associative memory\n- **🔄 Long-term / Short-term Memory**: Distinguish between temporary details and important knowledge\n- **🗂️ Memory Zones**: Organize information into separate domains (projects, clients, topics)\n- **🔒 Reliable & Scalable**: Built on Elasticsearch for enterprise-grade performance\n\n## 🚀 5-Minute Setup\n\nGetting started is incredibly simple:\n\n### Prerequisites\n\n- **Docker**: Required for running Elasticsearch (or a local Elasticsearch installation)\n- **Node.js**: Version 18 or higher\n- **npm**: For package management\n\n```bash\n# 1. Clone the repository\ngit clone https://github.com/mcp-servers/mcp-servers.git\ncd mcp-servers/memory\n\n# 2. Install dependencies\nnpm install\n\n# 3. Start Elasticsearch (uses Docker)\nnpm run es:start\n# Note: If you prefer to use your own Elasticsearch installation,\n# set the ES_NODE environment variable to point to your Elasticsearch instance\n\n# 4. Build the project\nnpm run build\n```\n\n### 🔌 Connecting to Claude Desktop\n\nMCP Memory is designed to work seamlessly with Claude Desktop, giving Claude persistent memory across all your conversations:\n\n1. **Copy and configure the launch script**:\n \n The repository includes a `launch.example` file that you can simply copy:\n \n ```bash\n # Copy the example launch file\n cp launch.example launch.sh\n \n # Edit launch.sh to add your Groq API key\n # This is required for smart memory retrieval\n nano launch.sh # or use your preferred editor\n ```\n \n Make the script executable:\n ```bash\n chmod +x launch.sh\n ```\n\n2. **Add the command to Claude Desktop**:\n - Open Claude Desktop Settings\n - Navigate to the \"Commands\" section\n - Click \"Add New Command\"\n - Configure as follows:\n - **Name**: MCP Memory\n - **Command**: /path/to/mcp-servers/memory/launch.sh\n - **Arguments**: Leave empty\n - **Run in background**: Yes\n - **Show in menu**: Yes\n\n3. **Verify connection**:\n - Start the command from Claude Desktop\n - You should see a notification that Claude is connected to MCP Memory\n - Try asking Claude about something you discussed in a previous conversation!\n\nFor complete examples and visual guides, see the [Claude Desktop MCP Server Setup Guide](https://github.com/anthropic-claude/claude-desktop-mcp-examples) online.\n\n## 💡 How It Works\n\nMCP Memory creates a structured knowledge graph where:\n\n1. **Entities** represent people, concepts, projects, or anything worth remembering\n2. **Relations** connect entities, creating a network of associations\n3. **Observations** capture specific details about entities\n4. **Relevance scoring** determines what information to prioritize\n\nWhen integrated with an LLM, the system automatically:\n- Stores new information learned during conversations\n- Retrieves relevant context when needed\n- Builds connections between related concepts\n- Forgets unimportant details while preserving critical knowledge\n\n## 🛠️ Example: How Agents Use Memory\n\n### From the User's Perspective\n\n**Conversation 1: Initial Information**\n\n```\nUser: My daughter's name is Emma and she's turning 8 next month.\n\nClaude: That's wonderful! Emma will be turning 8 next month. Is there anything special you're planning for her birthday?\n(Behind the scenes: Claude stores \"Emma\" as an entity of type \"Person\" with observations about her age and birthday timing)\n```\n\n**Conversation 2: Days or Weeks Later**\n\n```\nUser: I'm thinking about what to get for a gift.\n\nClaude: If you're looking for gift ideas for Emma's 8th birthday coming up, have you considered what her current interests are? At that age, many children enjoy creative items, books, or educational games.\n(Behind the scenes: Claude retrieved the entity \"Emma\", her age, and the fact that her birthday is approaching)\n```\n\n**Conversation 3: After the Birthday**\n\n```\nUser: The party went really well on Saturday!\n\nClaude: I'm glad Emma's 8th birthday party was a success on Saturday! Did she enjoy the gifts she received?\n(Behind the scenes: Claude updates the observations about Emma, noting that her birthday party occurred on a Saturday and went well)\n```\n\n### How the Agent Uses Memory\n\nWhen the user mentions something important, the agent:\n\n1. **Recognizes important information** worth remembering\n2. **Stores it in memory** by creating entities, relations, and observations\n3. **Updates existing information** when new details emerge\n\nWhen the user mentions something related to stored information, the agent:\n\n1. **Searches memory** for relevant context based on the current conversation\n2. **Retrieves important details** that might be helpful\n3. **Incorporates this information** naturally into its responses\n\nThis happens automatically - the user simply has a normal conversation with the assistant, and the memory system works behind the scenes to maintain context across sessions.\n\n### Intelligent Entity Management\n\nMCP Memory includes smart handling of entity creation and updates:\n\n- When attempting to create an entity that already exists, the system returns the existing entity data with guidance on how to extend it with new information\n- The system intelligently differentiates between creating new entities and updating existing ones\n- Entity relationships are automatically maintained even when information is updated\n\n## 🧰 Admin Tools\n\nMCP Memory includes a comprehensive admin CLI for maintaining your knowledge graph:\n\n```bash\n# Search the memory\nnode dist/admin-cli.js search \"Emma birthday\"\n\n# View details about a specific entity\nnode dist/admin-cli.js entity \"Emma\"\n\n# Back up your entire memory system\nnode dist/admin-cli.js backup memory-backup.json\n```\n\n## 📚 Advanced Features\n\n### Memory Zones\n\nOrganize knowledge into separate domains:\n\n```bash\n# Create a project-specific memory zone\nnode dist/admin-cli.js zones add client-acme \"ACME Corp Project Knowledge\"\n\n# Import data into that zone\nnode dist/admin-cli.js import acme-data.json client-acme\n```\n\n### Conversational Memory Management\n\nYou can also instruct the assistant to organize memories in different zones through natural conversation:\n\n**Creating and Using Memory Zones**\n\n```\nUser: Claude, can you create a new memory zone called \"work-project\" for storing information about my current work project?\n\nClaude: I've created a new memory zone called \"work-project\". I'll store information related to your work project in this zone. What would you like me to remember about your project?\n\nUser: The project deadline is March 15th, and we're building a new inventory management system.\n\nClaude: I've stored information about your work project in the \"work-project\" zone, including the March 15th deadline and that you're building an inventory management system. I'll keep future information about this project organized in this zone.\n(Behind the scenes: Claude creates a new memory zone and stores project information specifically in that zone)\n```\n\n**Retrieving Zone-Specific Information**\n\n```\nUser: What do you remember about my work project?\n\nClaude: Based on what we've discussed about your work project, I recall that you're building an inventory management system with a deadline of March 15th.\n(Behind the scenes: Claude specifically searches the \"work-project\" zone for relevant information)\n```\n\n**Switching Between Memory Zones**\n\n```\nUser: Claude, let's switch to my \"cooking\" memory zone now. I want to discuss a recipe.\n\nClaude: I've switched to your \"cooking\" memory zone. What recipe would you like to discuss today?\n(Behind the scenes: Claude updates its memory context to prioritize searching in the \"cooking\" zone)\n\nUser: What was that pasta dish I told you about last month?\n\nClaude: From our previous conversations in your cooking zone, you shared a carbonara recipe that used pancetta, eggs, pecorino cheese, and black pepper. You mentioned it was a family favorite that you learned during your trip to Rome.\n(Behind the scenes: Claude searches specifically in the \"cooking\" zone to find this information)\n```\n\nBy organizing memory into separate zones, conversations become more relevant and focused on the current topic or project.\n\n### Search Capabilities\n\nLeverage Elasticsearch's powerful search features:\n\n```bash\n# Fuzzy search (finds \"meeting\" even with typo)\nnode dist/admin-cli.js search \"meteing notes\"\n\n# Zone-specific search\nnode dist/admin-cli.js search \"budget\" client-acme\n```\n\n## 🤝 Contributing\n\nContributions are welcome! See [CONTRIBUTING.md](CONTRIBUTING.md) for details.\n\n## 📝 License\n\nMIT\n\n---\n\n

\n Ready to give your AI a memory that lasts? Get started in 5 minutes!
\n GitHub •\n Discord •\n Documentation\n

\n","url","https://github.com/j3k0/mcp-elastic-memory","githubRepository",{"_84":85,"_86":87,"_88":89,"_90":91,"_92":93,"_94":95,"_99":100,"_101":-5,"_102":103},"defaultBranch","main","fullName","j3k0/mcp-brain-tools","githubTreeId",105107,"language","TypeScript","name","mcp-brain-tools","owner",{"_96":97,"_17":98},"login","j3k0","alygxmjf4j","recentStargazers",1,"spdxLicense","stargazers",6,"glamaJson","id",1588,"inspectable",true,"integrations",[111,121],{"_112":113,"_41":119,"_17":120},"brand",{"_114":115,"_92":116,"_117":118},"logoSvgPath","M13.394 0C8.683 0 4.609 2.716 2.644 6.667h15.641a4.77 4.77 0 0 0 3.073-1.11c.446-.375.864-.785 1.247-1.243l.001-.002A11.974 11.974 0 0 0 13.394 0zM1.804 8.889a12.009 12.009 0 0 0 0 6.222h14.7a3.111 3.111 0 1 0 0-6.222zm.84 8.444C4.61 21.283 8.684 24 13.395 24c3.701 0 7.011-1.677 9.212-4.312l-.001-.002a9.958 9.958 0 0 0-1.247-1.243 4.77 4.77 0 0 0-3.073-1.11z","Elasticsearch","slug","elasticsearch","Uses Elasticsearch as the backend for the knowledge graph, providing distributed, scalable storage for entities and relations with advanced search capabilities","q569wx7moj",{"_112":122,"_41":125,"_17":126},{"_114":123,"_92":91,"_117":124},"M1.125 0C.502 0 0 .502 0 1.125v21.75C0 23.498.502 24 1.125 24h21.75c.623 0 1.125-.502 1.125-1.125V1.125C24 .502 23.498 0 22.875 0zm17.363 9.75c.612 0 1.154.037 1.627.111a6.38 6.38 0 0 1 1.306.34v2.458a3.95 3.95 0 0 0-.643-.361 5.093 5.093 0 0 0-.717-.26 5.453 5.453 0 0 0-1.426-.2c-.3 0-.573.028-.819.086a2.1 2.1 0 0 0-.623.242c-.17.104-.3.229-.393.374a.888.888 0 0 0-.14.49c0 .196.053.373.156.529.104.156.252.304.443.444s.423.276.696.41c.273.135.582.274.926.416.47.197.892.407 1.266.628.374.222.695.473.963.753.268.279.472.598.614.957.142.359.214.776.214 1.253 0 .657-.125 1.21-.373 1.656a3.033 3.033 0 0 1-1.012 1.085 4.38 4.38 0 0 1-1.487.596c-.566.12-1.163.18-1.79.18a9.916 9.916 0 0 1-1.84-.164 5.544 5.544 0 0 1-1.512-.493v-2.63a5.033 5.033 0 0 0 3.237 1.2c.333 0 .624-.03.872-.09.249-.06.456-.144.623-.25.166-.108.29-.234.373-.38a1.023 1.023 0 0 0-.074-1.089 2.12 2.12 0 0 0-.537-.5 5.597 5.597 0 0 0-.807-.444 27.72 27.72 0 0 0-1.007-.436c-.918-.383-1.602-.852-2.053-1.405-.45-.553-.676-1.222-.676-2.005 0-.614.123-1.141.369-1.582.246-.441.58-.804 1.004-1.089a4.494 4.494 0 0 1 1.47-.629 7.536 7.536 0 0 1 1.77-.201zm-15.113.188h9.563v2.166H9.506v9.646H6.789v-9.646H3.375z","typescript","Provides a TypeScript interface to Elasticsearch with all core operations for interacting with the knowledge graph","8zdoq36mrw","Elasticsearch Knowledge Graph for MCP","namespace","npmPackage","prompts","redditPostForeignId","1j6w4ky","relatedMcpServerIds",[135,136,137,138,139,140],1255,229,962,1382,437,1384,"resources","scores",{"_144":145,"_146":147,"_148":147},"license",0,"quality",100,"security","simpleIcon","supportedPlatforms","tools",[153,187,207,222,253,269,302,313,326,341,352,357,367,378,399,414,435],{"_92":154,"_41":155,"_156":157},"create_entities","Create entities in knowledge graph (memory)","inputSchema",{"_43":63,"_158":159,"_61":160,"_35":162,"_186":28},"$schema","http://json-schema.org/draft-07/schema#",[161],"entities",{"_161":163,"_183":184},{"_43":164,"_165":166,"_41":182},"array","items",{"_43":63,"_61":167,"_35":169},[92,168],"entityType",{"_92":170,"_168":172,"_174":175,"_178":179},{"_43":44,"_41":171},"Entity name",{"_43":44,"_41":173},"Entity type","observations",{"_43":164,"_165":176,"_41":177},{"_43":44},"Observations about this entity","relevanceScore",{"_43":180,"_41":181},"number","Relevance score (higher = more important)","List of entities to create","memory_zone",{"_43":44,"_41":185},"Optional memory zone to create entities in. If not specified, uses the default zone.","additionalProperties",{"_92":188,"_41":189,"_156":190},"update_entities","Update entities in knowledge graph (memory)",{"_43":63,"_158":159,"_61":191,"_35":192,"_186":28},[161],{"_161":193,"_183":205},{"_43":164,"_165":194,"_41":204},{"_43":63,"_61":195,"_35":196},[92],{"_92":197,"_168":198,"_199":200,"_174":202},{"_43":44},{"_43":44},"isImportant",{"_43":201},"boolean",{"_43":164,"_165":203},{"_43":44},"List of entities to update",{"_43":44,"_41":206},"Optional memory zone specifier. If provided, entities will be updated in this zone.",{"_92":208,"_41":209,"_156":210},"delete_entities","Delete entities from knowledge graph (memory)",{"_43":63,"_158":159,"_61":211,"_35":213,"_186":28},[212],"names",{"_212":214,"_183":217,"_219":220},{"_43":164,"_165":215,"_41":216},{"_43":44},"Names of entities to delete",{"_43":44,"_41":218},"Optional memory zone specifier. If provided, entities will be deleted from this zone.","cascade_relations",{"_43":201,"_39":108,"_41":221},"Whether to delete relations involving these entities (default: true)",{"_92":223,"_41":224,"_156":225},"create_relations","Create relationships between entities in knowledge graph (memory)",{"_43":63,"_158":159,"_61":226,"_35":228,"_186":28},[227],"relations",{"_227":229,"_183":248,"_250":251},{"_43":164,"_165":230,"_41":247},{"_43":63,"_61":231,"_35":234},[232,233,43],"from","to",{"_233":235,"_232":237,"_43":239,"_241":242,"_244":245},{"_43":44,"_41":236},"Target entity name",{"_43":44,"_41":238},"Source entity name",{"_43":44,"_41":240},"Relationship type","toZone",{"_43":44,"_41":243},"Optional zone for target entity, defaults to memory_zone or default zone. Must be one of the existing zones.","fromZone",{"_43":44,"_41":246},"Optional zone for source entity, defaults to memory_zone or default zone. Must be one of the existing zones.","List of relations to create",{"_43":44,"_41":249},"Optional default memory zone specifier. Used if a relation doesn't specify fromZone or toZone.","auto_create_missing_entities",{"_43":201,"_39":108,"_41":252},"Whether to automatically create missing entities in the relations (default: true)",{"_92":254,"_41":255,"_156":256},"delete_relations","Delete relationships from knowledge graph (memory)",{"_43":63,"_158":159,"_61":257,"_35":258,"_186":28},[227],{"_227":259,"_183":267},{"_43":164,"_165":260,"_41":266},{"_43":63,"_61":261,"_35":262},[232,233,43],{"_233":263,"_232":264,"_43":265},{"_43":44,"_41":236},{"_43":44,"_41":238},{"_43":44,"_41":240},"List of relations to delete",{"_43":44,"_41":268},"Optional memory zone specifier. If provided, relations will be deleted from this zone.",{"_92":270,"_41":271,"_156":272},"search_nodes","Search entities using ElasticSearch query syntax. Supports boolean operators (AND, OR, NOT), fuzzy matching (~), phrases (\"term\"), proximity (\"terms\"~N), wildcards (*, ?), and boosting (^N). Examples: 'meeting AND notes', 'Jon~', '\"project plan\"~2'. All searches respect zone isolation.",{"_43":63,"_158":159,"_61":273,"_35":276,"_186":28},[274,275],"query","informationNeeds",{"_277":278,"_274":281,"_283":284,"_291":292,"_183":295,"_275":297,"_299":300},"limit",{"_43":279,"_41":280},"integer","Max results (default: 20, or 5 with observations).",{"_43":44,"_41":282},"ElasticSearch query string. Use '*' for all entities.","sortBy",{"_285":286,"_43":44,"_41":290},"enum",[287,288,289],"relevance","recency","importance","Sort by match quality, access time, or importance.","entityTypes",{"_43":164,"_165":293,"_41":294},{"_43":44},"Filter to specific entity types (OR condition if multiple).",{"_43":44,"_41":296},"Limit search to specific zone. Omit for default zone.",{"_43":44,"_41":298},"Important. Describe what information you are looking for. What questions are you trying to answer? Helps get more useful results.","includeObservations",{"_43":201,"_39":28,"_41":301},"Include full entity observations (default: false).",{"_92":303,"_41":304,"_156":305},"open_nodes","Get details about specific entities in knowledge graph (memory) and their relations",{"_43":63,"_158":159,"_61":306,"_35":307,"_186":28},[212],{"_212":308,"_183":311},{"_43":164,"_165":309,"_41":310},{"_43":44},"Names of entities to retrieve",{"_43":44,"_41":312},"Optional memory zone to retrieve entities from. If not specified, uses the default zone.",{"_92":314,"_41":315,"_156":316},"add_observations","Add observations to an existing entity in knowledge graph (memory)",{"_43":63,"_158":159,"_61":317,"_35":318,"_186":28},[92,174],{"_92":319,"_183":321,"_174":323},{"_43":44,"_41":320},"Name of entity to add observations to",{"_43":44,"_41":322},"Optional memory zone where the entity is stored. If not specified, uses the default zone.",{"_43":164,"_165":324,"_41":325},{"_43":44},"Observations to add to the entity",{"_92":327,"_41":328,"_156":329},"mark_important","Mark entity as important in knowledge graph (memory) by boosting its relevance score",{"_43":63,"_158":159,"_61":330,"_35":332,"_186":28},[92,331],"important",{"_92":333,"_331":334,"_336":337,"_183":339},{"_43":44,"_41":171},{"_43":201,"_41":335},"Set as important (true - multiply relevance by 10) or not (false - divide relevance by 10)","auto_create",{"_43":201,"_39":28,"_41":338},"Whether to automatically create the entity if it doesn't exist (default: false)",{"_43":44,"_41":340},"Optional memory zone specifier. If provided, entity will be marked in this zone.",{"_92":342,"_41":343,"_156":344},"get_recent","Get recently accessed entities from knowledge graph (memory) and their relations",{"_43":63,"_158":159,"_35":345,"_186":28},{"_277":346,"_183":348,"_299":350},{"_43":279,"_41":347},"Max results (default: 20 if includeObservations is false, 5 if true)",{"_43":44,"_41":349},"Optional memory zone to get recent entities from. If not specified, uses the default zone.",{"_43":201,"_39":28,"_41":351},"Whether to include full entity observations in results (default: false)",{"_92":353,"_41":354,"_156":355},"list_zones","List all available memory zones with metadata.",{"_43":63,"_35":356},{},{"_92":358,"_41":359,"_156":360},"create_zone","Create a new memory zone with optional description.",{"_43":63,"_61":361,"_35":362},[92],{"_92":363,"_41":365},{"_43":44,"_41":364},"Zone name (cannot be 'default')",{"_43":44,"_41":366},"Optional zone description",{"_92":368,"_41":369,"_156":370},"delete_zone","Delete a memory zone and all its entities/relations.",{"_43":63,"_61":371,"_35":373},[92,372],"confirm",{"_92":374,"_372":376},{"_43":44,"_41":375},"Zone name to delete (cannot be 'default')",{"_43":201,"_39":28,"_41":377},"Confirmation flag, must be true",{"_92":379,"_41":380,"_156":381},"copy_entities","Copy entities between zones with optional relation handling.",{"_43":63,"_61":382,"_35":385},[212,383,384],"source_zone","target_zone",{"_212":386,"_389":390,"_383":392,"_384":394,"_396":397},{"_43":164,"_165":387,"_41":388},{"_43":44},"Entity names to copy","overwrite",{"_43":201,"_39":28,"_41":391},"Overwrite if entity exists (default: false)",{"_43":44,"_41":393},"Source zone",{"_43":44,"_41":395},"Target zone","copy_relations",{"_43":201,"_39":108,"_41":398},"Copy related relationships (default: true)",{"_92":400,"_41":401,"_156":402},"move_entities","Move entities between zones (copy + delete from source).",{"_43":63,"_61":403,"_35":404},[212,383,384],{"_212":405,"_389":408,"_383":409,"_384":410,"_411":412},{"_43":164,"_165":406,"_41":407},{"_43":44},"Entity names to move",{"_43":201,"_39":28,"_41":391},{"_43":44,"_41":393},{"_43":44,"_41":395},"move_relations",{"_43":201,"_39":108,"_41":413},"Move related relationships (default: true)",{"_92":415,"_41":416,"_156":417},"merge_zones","Merge multiple zones with conflict resolution options.",{"_43":63,"_61":418,"_35":420},[419,384],"source_zones",{"_384":421,"_419":423,"_426":427,"_429":430},{"_43":44,"_41":422},"Target zone to merge into",{"_43":164,"_165":424,"_41":425},{"_43":44},"Source zones to merge from","delete_source_zones",{"_43":201,"_39":28,"_41":428},"Delete source zones after merging","overwrite_conflicts",{"_285":431,"_43":44,"_39":432,"_41":434},[432,389,433],"skip","rename","How to handle name conflicts",{"_92":436,"_41":437,"_156":438},"zone_stats","Get statistics for entities and relationships in a zone.",{"_43":63,"_35":439},{"_440":441},"zone",{"_43":44,"_41":442},"Zone name (omit for default zone)","h9w4cyfdqx","updatedAt",["D",1743627670118],"meta",[448,451,453,460,463,465,468,471,474,477,480,482,485,488,490,492,494,496,498],{"_449":450},"title","Elasticsearch Knowledge Graph for MCP | Glama",{"_452":74,"_92":41},"content",{"_454":455,"_456":457,"_458":459},"href","https://glama.ai/mcp/servers/@j3k0/mcp-brain-tools","rel","canonical","tagName","link",{"_452":127,"_461":462},"property","og:title",{"_452":74,"_461":464},"og:description",{"_452":466,"_461":467},"https://glama.ai/logo.png","og:logo",{"_452":469,"_461":470},"https://glama.ai/mcp/servers/@j3k0/mcp-brain-tools/og-image","og:image",{"_452":472,"_461":473},"630","og:image:height",{"_452":475,"_461":476},"1200","og:image:width",{"_452":478,"_461":479},"website","og:type",{"_452":455,"_461":481},"og:url",{"_452":483,"_461":484},"ChatGPT alternative for power users","og:site_name",{"_452":486,"_92":487},"summary_large_image","twitter:card",{"_452":483,"_92":489},"twitter:title",{"_452":74,"_92":491},"twitter:description",{"_452":469,"_92":493},"twitter:image",{"_452":472,"_92":495},"twitter:image:height",{"_452":475,"_92":497},"twitter:image:width",{"_499":500},"script:ld+json",{"_501":502,"_503":504},"@context","http://schema.org","@graph",[505],{"_501":506,"_507":508,"_509":510},"https://schema.org","@type","BreadcrumbList","itemListElement",[511,517,521,525],{"_507":512,"_513":514,"_92":515,"_516":100},"ListItem","item","https://glama.ai/","Glama","position",{"_507":512,"_513":518,"_92":519,"_516":520},"https://glama.ai/mcp","MCP",2,{"_507":512,"_513":522,"_92":523,"_516":524},"https://glama.ai/mcp/servers","Servers",3,{"_507":512,"_513":455,"_92":127,"_516":526},4,"routes/_public/mcp/servers/~namespace/~slug/_pages/_explorer/blob/~branch/~/_route",{"_529":530,"_534":85,"_29":535,"_713":714,"_715":716},"blob",{"_452":531,"_532":533},"#!/bin/bash\nset -e\nexport GROQ_API_KEY=gsk_your_key\ncd \"$(dirname \"$0\")\"\ndocker compose up -d elasticsearch\ntest -e node_modules || npm install\nnpm run build\nnode dist/index.js \"$@\"\n","fileName","launch.example","branch",{"_31":536,"_33":537,"_13":546,"_73":74,"_75":74,"_76":547,"_82":548,"_104":-5,"_105":106,"_107":108,"_109":550,"_92":127,"_128":97,"_129":-5,"_130":-5,"_131":132,"_133":555,"_141":-5,"_142":556,"_149":-5,"_117":93,"_150":-5,"_151":557,"_17":443,"_444":712},["D",1741480651197],{"_35":538,"_61":545,"_43":63},{"_37":539,"_45":540,"_48":541,"_51":542,"_54":543,"_58":544},{"_39":40,"_41":42,"_43":44},{"_41":47,"_43":44},{"_41":50,"_43":44},{"_39":39,"_41":53,"_43":44},{"_39":56,"_41":57,"_43":44},{"_41":60,"_43":44},[],[65,66,67,68,69,70,71,72],{"_78":79,"_80":81},{"_84":85,"_86":87,"_88":89,"_90":91,"_92":93,"_94":549,"_99":100,"_101":-5,"_102":103},{"_96":97,"_17":98},[551,553],{"_112":552,"_41":119,"_17":120},{"_114":115,"_92":116,"_117":118},{"_112":554,"_41":125,"_17":126},{"_114":123,"_92":91,"_117":124},[135,136,137,138,139,140],{"_144":145,"_146":147,"_148":147},[558,572,586,594,609,621,634,641,649,657,663,666,672,678,688,698,708],{"_92":154,"_41":155,"_156":559},{"_43":63,"_158":159,"_61":560,"_35":561,"_186":28},[161],{"_161":562,"_183":571},{"_43":164,"_165":563,"_41":182},{"_43":63,"_61":564,"_35":565},[92,168],{"_92":566,"_168":567,"_174":568,"_178":570},{"_43":44,"_41":171},{"_43":44,"_41":173},{"_43":164,"_165":569,"_41":177},{"_43":44},{"_43":180,"_41":181},{"_43":44,"_41":185},{"_92":188,"_41":189,"_156":573},{"_43":63,"_158":159,"_61":574,"_35":575,"_186":28},[161],{"_161":576,"_183":585},{"_43":164,"_165":577,"_41":204},{"_43":63,"_61":578,"_35":579},[92],{"_92":580,"_168":581,"_199":582,"_174":583},{"_43":44},{"_43":44},{"_43":201},{"_43":164,"_165":584},{"_43":44},{"_43":44,"_41":206},{"_92":208,"_41":209,"_156":587},{"_43":63,"_158":159,"_61":588,"_35":589,"_186":28},[212],{"_212":590,"_183":592,"_219":593},{"_43":164,"_165":591,"_41":216},{"_43":44},{"_43":44,"_41":218},{"_43":201,"_39":108,"_41":221},{"_92":223,"_41":224,"_156":595},{"_43":63,"_158":159,"_61":596,"_35":597,"_186":28},[227],{"_227":598,"_183":607,"_250":608},{"_43":164,"_165":599,"_41":247},{"_43":63,"_61":600,"_35":601},[232,233,43],{"_233":602,"_232":603,"_43":604,"_241":605,"_244":606},{"_43":44,"_41":236},{"_43":44,"_41":238},{"_43":44,"_41":240},{"_43":44,"_41":243},{"_43":44,"_41":246},{"_43":44,"_41":249},{"_43":201,"_39":108,"_41":252},{"_92":254,"_41":255,"_156":610},{"_43":63,"_158":159,"_61":611,"_35":612,"_186":28},[227],{"_227":613,"_183":620},{"_43":164,"_165":614,"_41":266},{"_43":63,"_61":615,"_35":616},[232,233,43],{"_233":617,"_232":618,"_43":619},{"_43":44,"_41":236},{"_43":44,"_41":238},{"_43":44,"_41":240},{"_43":44,"_41":268},{"_92":270,"_41":271,"_156":622},{"_43":63,"_158":159,"_61":623,"_35":624,"_186":28},[274,275],{"_277":625,"_274":626,"_283":627,"_291":629,"_183":631,"_275":632,"_299":633},{"_43":279,"_41":280},{"_43":44,"_41":282},{"_285":628,"_43":44,"_41":290},[287,288,289],{"_43":164,"_165":630,"_41":294},{"_43":44},{"_43":44,"_41":296},{"_43":44,"_41":298},{"_43":201,"_39":28,"_41":301},{"_92":303,"_41":304,"_156":635},{"_43":63,"_158":159,"_61":636,"_35":637,"_186":28},[212],{"_212":638,"_183":640},{"_43":164,"_165":639,"_41":310},{"_43":44},{"_43":44,"_41":312},{"_92":314,"_41":315,"_156":642},{"_43":63,"_158":159,"_61":643,"_35":644,"_186":28},[92,174],{"_92":645,"_183":646,"_174":647},{"_43":44,"_41":320},{"_43":44,"_41":322},{"_43":164,"_165":648,"_41":325},{"_43":44},{"_92":327,"_41":328,"_156":650},{"_43":63,"_158":159,"_61":651,"_35":652,"_186":28},[92,331],{"_92":653,"_331":654,"_336":655,"_183":656},{"_43":44,"_41":171},{"_43":201,"_41":335},{"_43":201,"_39":28,"_41":338},{"_43":44,"_41":340},{"_92":342,"_41":343,"_156":658},{"_43":63,"_158":159,"_35":659,"_186":28},{"_277":660,"_183":661,"_299":662},{"_43":279,"_41":347},{"_43":44,"_41":349},{"_43":201,"_39":28,"_41":351},{"_92":353,"_41":354,"_156":664},{"_43":63,"_35":665},{},{"_92":358,"_41":359,"_156":667},{"_43":63,"_61":668,"_35":669},[92],{"_92":670,"_41":671},{"_43":44,"_41":364},{"_43":44,"_41":366},{"_92":368,"_41":369,"_156":673},{"_43":63,"_61":674,"_35":675},[92,372],{"_92":676,"_372":677},{"_43":44,"_41":375},{"_43":201,"_39":28,"_41":377},{"_92":379,"_41":380,"_156":679},{"_43":63,"_61":680,"_35":681},[212,383,384],{"_212":682,"_389":684,"_383":685,"_384":686,"_396":687},{"_43":164,"_165":683,"_41":388},{"_43":44},{"_43":201,"_39":28,"_41":391},{"_43":44,"_41":393},{"_43":44,"_41":395},{"_43":201,"_39":108,"_41":398},{"_92":400,"_41":401,"_156":689},{"_43":63,"_61":690,"_35":691},[212,383,384],{"_212":692,"_389":694,"_383":695,"_384":696,"_411":697},{"_43":164,"_165":693,"_41":407},{"_43":44},{"_43":201,"_39":28,"_41":391},{"_43":44,"_41":393},{"_43":44,"_41":395},{"_43":201,"_39":108,"_41":413},{"_92":415,"_41":416,"_156":699},{"_43":63,"_61":700,"_35":701},[419,384],{"_384":702,"_419":703,"_426":705,"_429":706},{"_43":44,"_41":422},{"_43":164,"_165":704,"_41":425},{"_43":44},{"_43":201,"_39":28,"_41":428},{"_285":707,"_43":44,"_39":432,"_41":434},[432,389,433],{"_92":436,"_41":437,"_156":709},{"_43":63,"_35":710},{"_440":711},{"_43":44,"_41":442},["D",1743627670118],"path","","treeMembers",[717,720,722,724,726,728,730,732,734,736,738,739,741,743,745],{"_713":718,"_43":719},".cursor","tree",{"_713":721,"_43":719},"legacy",{"_713":723,"_43":719},"src",{"_713":725,"_43":719},"tests",{"_713":727,"_43":529},"BUILD-NOTES.md",{"_713":729,"_43":529},"docker-compose.yml",{"_713":731,"_43":529},"Dockerfile",{"_713":733,"_43":529},".gitignore",{"_713":735,"_43":529},"jest.config.cjs",{"_713":737,"_43":529},"jest.config.js",{"_713":533,"_43":529},{"_713":740,"_43":529},"package.json",{"_713":742,"_43":529},"README.md",{"_713":744,"_43":529},"tsconfig.json",{"_713":746,"_43":529},"vitest.config.ts","actionData","errors"]