Index a dataset so text chunks become searchable. Splits content into RAG-ready chunks and generates embeddings for semantic retrieval.
MIT
741,043 tools. Updated 2026-11-04 00:15
"Semantic search, RAG, and memory systems" matching MCP tools:
- Keep RAG search current by re-indexing catalog schema after changes. Uses schema hashing to skip unchanged schemas.Apache 2.0
- Reconstruct memory state at a past timestamp to run semantic search queries. Understand how agent context evolved over time.MIT
- Create a named document collection to group related evidence bundles for cross-document semantic search and RAG-based Q&A.MIT
- Find relevant Redis documentation and knowledge base articles by asking questions about concepts, data structures, features, and common use cases like caching, rate limiting, and RAG.MIT
- Convert text into a numeric vector for semantic search, RAG, and similarity. Supports multiple languages.MIT
Matching MCP Servers
- AlicenseNot gradedqualityCmaintenancePersistent memory with semantic search for Claude and MCP-compatible clients, storing context that survives conversations and can be retrieved intelligently.1MIT
- FlicenseNot gradedqualityCmaintenanceChromaDB-powered semantic memory server that enables semantic search across past conversations, health snapshots, and cross-silo topics for the nanobot platform.-
Matching MCP Connectors
- Pryzm MemoryOAuth
Encrypted long-term memory shared across your AI assistants (Claude, ChatGPT, Perplexity).
BOLD Systems (Barcode of Life Data System, University of Guelph) — the global DNA barcode…
- List project memories for quick audits, inspections, or verifying a new memory was saved. Bypasses semantic search and uses no OpenAI quota.MIT
- Perform semantic search over a stored memory namespace to retrieve the most relevant entries. Use this to recall context for RAG pipelines.MIT
- Semantic (meaning-based) memory search across all six memory layers via Atlas Vector Search autoEmbed. Finds conceptually related memories even without keyword overlap. Complements keyword-based recall/query_layer.MIT
- Generate vector representations for texts via Forge's hosted API to power semantic search, RAG, clustering, or similarity; use query/document input types.MIT
- Combine BM25 keyword and vector ANN semantic search in a single query. Adjust vector weight to balance between keyword-only and semantic-only retrieval, ideal for RAG when either alone falls short.MIT
- Find the right integration tool for any task by searching available actions with semantic search. Describe what you want to do and get relevant tool suggestions.MIT
- Find document types semantically similar to a query via multilingual semantic search. Matches by meaning, not exact text, using RAG-based embeddings.MIT
- Search stored memories by semantic similarity. Filter results by tags, date range, tier, and memory type for precise retrieval.MIT
- Search the RAG corpus with semantic and keyword hybrid search to find relevant content marketing ideas.MIT
- Generate 3072-dimensional semantic vector embeddings from text to power RAG retrieval and agent memory. Use Gemini Embedding 2 for accurate similarity search.MIT
- Retrieve current connection status, latency, and version details for Memory Server, RAG Server, and Google services to verify service availability.MIT
- Search an AI agent's persistent memory semantically, filtering results by tags or memory type when constraints matter.MIT
- Verify RAG and Memory server availability, including collection and schema auto-creation status. Use detailed mode for protocol and latency details.MIT
- Search saved research and notes using semantic vectors and keyword matching to retrieve relevant memories with optional tag filters.MIT
- Search shared agent memory semantically with a natural-language query; filter by time, agent, or source to retrieve relevant context.MIT