Filter documents by metadata before ranking by vector similarity to enable production RAG and semantic search pipelines.
MIT
301,723 tools. Last updated 2026-07-15 10:39
"An MCP server for indexing S3 content into a RAG vector database" matching MCP tools:
- Upload a local file, chunk it, generate embeddings, and store the vectors in your vector database.MIT
- Scrape a web page, embed the content, and store vectors in your vector database in a single call. Add web data to your RAG pipeline for retrieval.MIT
- Retrieves all supported vector database providers, including required and optional config fields and setup notes. Use this to prepare the configuration needed for syncing data to a vector database.MIT
- Generate vector embeddings from text for semantic search, RAG, clustering, or similarity tasks. Choose between query or document input type and adjust model quality and dimensionality.MIT
- Convert a list of document strings into vector embeddings using the same model as memo's internal document embedder. Use for diagnostics or external indexing.MIT
Matching MCP Servers
- AlicenseBqualityBmaintenanceA production-grade MCP server for integrating RAG into AI agents, supporting multiple vector databases with enterprise security and dynamic tool selection.Last updated215MIT
- Alicense-qualityBmaintenanceIntegrates RAG into AI agents via MCP Server, supporting multiple vector database technologies for collection management and search operations.Last updatedMIT
Matching MCP Connectors
MCP server for social media and content data including social profiles, engagement metrics, content trends, and influencer analytics for AI agents.
Apple Developer Documentation with Semantic Search, RAG, and AI reranking for MCP clients
- Scrape a web page, divide its content into structured text chunks optimized for RAG retrieval, and return them ready for downstream processing. No embedding or vector database required.MIT
- Check whether the Othos MCP server has an Othos auth cookie or MCP personal token configured.MIT
- Crawl an entire domain, chunk every page, embed all chunks, and inject into your vector database in a single call.MIT
- Index new strategy research, marketplace listings, blueprints, skills, and bot logs into a self-hosted Onyx RAG host by triggering an incremental ingest.MIT
- Integrate S3 storage into your endpoint by adding S3 clients to its context. Provides access to S3_CLIENT, S3_DATA, S3_WEB, and S3_PUBLIC for file operations.Apache 2.0
- Create a vector index on a vector field in Baidu Vector Database to enable efficient similarity searches for AI applications.Apache 2.0
- Parse code files into semantic chunks such as functions, classes, and methods to improve retrieval in RAG systems.MIT
- Integrate a Milvus vector database into an endpoint's context, enabling vector search operations through ctx.MILVUS.Apache 2.0
- Retrieve a complete list of database names stored on the Doris MCP Server using a unique identifier for each tool call.Apache 2.0
- Search and filter RAG-capable MCP servers by query, categories, score, transport, and other criteria to find the right retrieval server for your task.MIT
- Get a high-level summary of the MCP server's capabilities, organized into buckets like schema introspection and query execution. Use this to discover the server's surface without exploring the full tool catalog.MIT
- Crawl an entire website, chunk each page, embed all chunks, and inject into your vector database in a single call.MIT
- Retrieve Memkin MCP server health metadata: database counts, version, and legacy tool exposure.Apache 2.0
- Verify connection and detect version drift for the SLAM Commerce MCP server by introspecting protocol versions, database status, and store metadata.MIT