Filter documents by metadata before ranking by vector similarity to enable production RAG and semantic search pipelines.
MIT
135,132 tools. Last updated 2026-05-25 22:23
"An MCP server for indexing S3 content into a RAG vector database" matching MCP tools:
- Confirm the Nordic MCP server process is responding. Use at the start of a session to verify server reachability before other calls.MIT
- Convert a note into a source document for NotebookLM, enabling its content to be used in RAG queries and research. Simply provide the note title.MIT
- Create a vector index on a vector field in Baidu Vector Database to enable efficient similarity searches for AI applications.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
Matching MCP Servers
- Flicense-qualityCmaintenanceAn MCP server that enables interacting with Google's Indexing API, allowing agents to submit URLs to Google for indexing or removal from search results through natural language commands.Last updated
- AlicenseAqualityCmaintenanceA Model Context Protocol (MCP) server for AWS S3 integration using FastMCP and boto3. This server provides access to S3 functionality through MCP-compatible tools.Last updated101MIT
Matching MCP Connectors
MCP server for social media and content data including social profiles, engagement metrics, content trends, and influencer analytics for AI agents.
Dev.to, Steam, podcasts, Eventbrite — cross-format content discovery for AI curators.
- Restore the memory database from a validated backup file, replacing the live database. Restart the MCP server after restore for changes to apply.MIT
- Create a vector store from S3 markdown files by downloading, chunking, embedding with AWS Bedrock Titan, and storing in PostgreSQL for semantic search.MIT
- Retrieve current status details for the Smart Coding MCP server, including version, workspace configuration, indexing progress, and cache information to monitor the semantic search system's operational state.MIT
- Add multiple context entries to a vector database in one batch operation for efficient bulk indexing and storage of semantic information.MIT
- Index all markdown and text files in a soul workspace into a local vector database for RAG. Uses SHA-256 hash manifest to incrementally update, skipping unchanged files. Creates .rag_index/ directory.Business Source 1.1
- Remove multiple objects from an AWS S3 bucket using the AWS MCP Server. Specify bucket name and object keys to delete files or data.BSD 3-Clause
- Upload local files to an S3 bucket for use as data sources in Amazon Bedrock Knowledge Bases, enabling document ingestion for RAG applications.MIT
- Parse code files into semantic chunks such as functions, classes, and methods to improve retrieval in RAG systems.
- Import a model from Amazon S3 into Tuning Engines cloud storage to serve as a base model for fine-tuning jobs.MIT
- Upload files to process and index them for searchable knowledge retrieval using RAG (Retrieval-Augmented Generation) technology.MIT
- Modify existing documents in a knowledge base by replacing content and re-indexing for accurate retrieval in local RAG systems.MIT
- Upload text content to an S3 bucket object using AWS MCP Server. Specify bucket, key, and content with optional profile, region, and content type settings.BSD 3-Clause
- Stop language server indexing by removing a directory from the workspace.MIT
- Retrieve a paginated list of all documents stored in the Chroma vector database, specifying limit and offset for efficient navigation and management.MIT