Search memory files using hybrid, lexical, or vector methods to retrieve relevant information from stored data.
MIT
205,013 tools. Last updated 2026-06-15 02:11
"A server for handling vector memory operations" matching MCP tools:
- 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
- Create, list, delete collections and add documents in a vector database to support RAG operations.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
- Verify DataBeak server status and resource availability before operations. Returns server health, memory usage, session capacity, and version details to ensure system readiness.
Matching MCP Servers
- Alicense-qualityCmaintenanceA 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.Last updatedMIT
- Alicense-qualityDmaintenanceEnables AI assistants to save and recall information from files or free-form notes using natural language, acting as a long-term memory system.Last updatedMIT
Matching MCP Connectors
One memory, every AI. A shared, user-owned markdown memory your AI clients read and write over MCP.
Cultural color and colour intelligence API. Every colour anchored to a named person, a documented year, and a consequence. 34 archives spanning literary, cultural, pigment, and national traditions. Ask it what color could get you executed in the Ottoman Empire.
- Handle file operations, run commands, analyze projects, and manage notebooks and vector stores through a single development interface.MIT
- Verify server connectivity and check rate limit status before batch operations. Returns server status, version, session ID, and rate limit details.MIT
- Retrieve the k nearest nodes to a query embedding using HNSW vector similarity. Use for semantic search after creating a vector index on a specific label and property.Apache 2.0
- Configure or view per-model permissions for ledger, memory, deploy, evidence, and secrets. Controls which operations each AI model can perform on these resources.MIT
- Check RSpace server connectivity and system health to verify accessibility before performing operations.AGPL 3.0
- Returns server health metadata for the active project to confirm the memory server is responsive before state operations.MIT
- Permanently remove incorrect or unwanted information from AI agent memory by ID, including deletion from vector index and knowledge graph.MIT
- Retrieve a chronological timeline of all memory operations, filterable by time range and operation type, including creates, updates, deletes, and rollbacks.MIT
- Scans and returns all Redis keys matching a given pattern by automatically handling multiple SCAN iterations. Safer than KEYS * for large datasets, but may use significant memory.MIT
- Parse CVSS vector strings (v2, v3.x, v4.0) into human-readable explanations for vulnerability severity assessment.Apache 2.0
- Retrieve the audit trail of memory operations. Filter by operation type and limit the number of entries.MIT
- Retrieve the OpenAPI specification for Adamik API to access exact schemas for blockchain operations across 80+ networks, including transaction formats, parameters, and error handling.MIT
- List all authenticated accounts configured for write operations on the Fediverse server.MIT
- Combine BM25 keyword search with vector ANN search in a single pass. Use for RAG when either semantic or keyword search alone is insufficient.MIT