Create a new context, either vector or memory, in a Lamatic project to store and retrieve data for AI workflows.
MIT
524,660 tools. Updated 2026-09-06 17:51
"A server for handling vector memory operations" matching MCP tools:
- Load a PDF into memory for session-based operations, making it accessible for redaction and other tools. Use this first to prepare a document for subsequent PDF operations.MIT
- Verify DataBeak server status and resource availability before operations. Returns server health, memory usage, session capacity, and version details to ensure system readiness.Apache 2.0
- Purge a specific visual state with its vector embeddings and perceptual hashes from the cache to reset memory or ensure privacy.MITDestructive
- Retrieve semantically similar memories by providing a memory ID; uses vector similarity to uncover hidden connections.PolyForm Noncommercial 1.0.0
- Scans Next.js projects for 'use server' functions to detect missing validation, error handling, authentication, and unprotected database calls.MIT
Matching MCP Servers
- AlicenseNot gradedqualityDmaintenanceA secure vector-based memory server that provides persistent semantic memory for AI assistants using sqlite-vec and sentence-transformers. It enables semantic search and organization of coding experiences, solutions, and knowledge with features like auto-cleanup and deduplication.MIT
- AlicenseNot gradedqualityDmaintenanceEnables AI assistants to save and recall information from files or free-form notes using natural language, acting as a long-term memory system.MIT
Matching MCP Connectors
Set handling guidance for a content item
Persistent semantic memory storage, associative recall, and recent memory index by namespace.
- Retrieve MongoDB server metrics including connections, memory, uptime, and operations to monitor database health.MIT
- Generate vector embeddings from text using BAAI BGE models. Useful for custom similarity calculations and manual vector operations.MIT
- Store a new memory with vector embedding, category, importance, and project scope, while conflict detection prevents duplicate or contradictory entries for reliable retrieval.MIT
- Search episodic memory to find past experiences similar to your query using vector similarity.MIT
- Retrieve the vector representation of a specific entity from the knowledge graph memory, enabling semantic search, similarity comparisons, and clustering operations.MIT
- Search memory files using hybrid, lexical, or vector methods to retrieve relevant information from stored data.MIT
- Process multiple operations concurrently with configurable concurrency, timeout, and error handling. Continue on failure or stop as needed, and optionally cache successful results.MIT
- Add a text fragment with optional metadata to a project's vector store, enabling long-term searchable memory.MIT
- Record a conversation or fact with emotional and contextual metadata into persistent memory using Markdown drawers and vector indexes.MIT
- Manage vector embeddings for Cursor10x MCP by performing store, search, update, and delete operations to enhance AI memory retention and recall.MIT
- Retrieve the vector embedding for a specific entity from your knowledge graph memory to enable semantic search and similarity comparisons.MIT
- Streamline development tasks with a universal tool for file operations, command execution, project analysis, notebook management, and vector store interactions. Simplify workflows through a unified interface integrated with Hanzo MCP.MIT
- Store and retrieve temporary data in memory using get, set, delete, and list operations. Data persists only while the server runs.-
- Save a new memory entry with vector embedding for semantic search and retrieval. Organize memories by title, content, tags, and topic ID.-
- Administer Codex app-server via MCP: manage accounts, config, apps, plugins, skills, environment, search, memory, real-time, and remote-control operations.-