"Connecting to SEC EDGAR MCP using Bedrock Models" matching MCP servers:

Enables management of Amazon Bedrock Knowledge Bases including creation, data source configuration, document ingestion, and RAG (Retrieval-Augmented Generation) queries with support for multiple embedding models and custom parsing/chunking strategies.

Turns GitHub repository history into a cited maintainer skill for coding agents, providing tools to collect evidence, query the knowledge graph, and inspect bundles.

Turn Obsidian notes into agent-ready context, preview-only plans, and safe repo handoffs.

Self-hostable MCP server for WhisperGraph — a graph of 7.39B nodes / 39B edges mapping DNS, BGP, GeoIP, WHOIS, and threat intelligence. Six read-only tools (Cypher query + schema introspection + threat assessment), six resources, eight investigation prompts. stdio and Streamable HTTP transports.

Enables AI tools to query context from a local JSON data source via stdio, demonstrating the Model Context Protocol.

MCP server that connects AI agents to Google NotebookLM, enabling natural language interaction with notebooks, including Q&A, source ingestion, and audio overview generation.

Enables local-first knowledge management for Codex, providing tools for guided discussions, contextual recall, review, and daily topics.

Builds a searchable memory bank from persona-specific PDF content and exposes it as an MCP tool for querying.

Provides tools for managing quantitative research knowledge graphs, enabling structured representation of research projects, datasets, variables, hypotheses, statistical tests, models, and results.

Enables local AI models to structure and reason through debates using argument maps, with claims and supporting/attacking arguments automatically validated and organized.

Provides full access to Hookmark on macOS, enabling users to create and manage bidirectional links between files, URLs, emails, and notes. It supports searching bookmarks, connecting items, and retrieving links from active applications through natural language.

Adds persistent memory to AI assistants by connecting to the Memphora cloud platform, allowing them to store and recall facts across conversations. It enables tools for searching memories, extracting insights, and maintaining long-term user context and preferences.

Extends large language model context length up to 60 times using gravitational memory, designed for Claude Code and Anthropic AI models.

MCP server that gives AI agents persistent, tenant-scoped memory with semantic search and consolidation, using CockroachDB vector indexes and Amazon Bedrock embeddings.

A server that provides data retrieval capabilities powered by Chroma embedding database, enabling AI models to create collections over generated data and user inputs, and retrieve that data using vector search, full text search, and metadata filtering.

Enables AI agents to persistently recall, apply, and reinforce solutions to previously solved problems across different models and tools, so mistakes are not repeated.

Provides access to the HUMMBL Base120 framework of 120 validated mental models organized across 6 transformations, enabling users to search, retrieve, and get AI-recommended mental models for problem-solving and decision-making.

A local-first personal archive with a read-only MCP gateway that preserves files and AI conversations, derives evidence-linked memory candidates using local models, and enables bounded context retrieval for questions.

This project implements a Model Context Protocol (MCP) server for connecting AI models with Obsidian knowledge bases. Through this server, AI models can directly access and manipulate Obsidian notes, including reading, creating, updating, and deleting notes, as well as managing folder structures.

A drop-in replacement for Anthropic's memory server that utilizes SQLite to ensure data integrity and concurrent access. It enhances the original functionality with semantic search capabilities using vector embeddings and ONNX models.