"Understanding MCP for IDA" matching MCP servers:

A self-contained, in-memory graph database for AI Agents. Provides semantic code understanding through the Model Context Protocol (MCP).

A local semantic memory and code-indexer that uses AST parsing for structural understanding and persists architectural decisions to help AI assistants bypass context window limits.

An MCP server that provides persistent memory for database schemas by storing table structures and metadata in a local SQLite file. It enables LLMs to save, retrieve, and manage database snapshots to maintain a structured understanding of database architectures.

Cline MCP integration that allows users to save, search, and format memories with semantic understanding, providing tools to store and retrieve information using vector embeddings for meaning-based search.

Provides a scalable knowledge graph implementation for Model Context Protocol using Elasticsearch, enabling AI models to store and query information with advanced search capabilities, memory-like behavior, and multi-zone architecture.

A Model Context Protocol server implementation that enables LLMs to interact with NebulaGraph database for graph exploration, supporting schema understanding, queries, and graph algorithms.

Enables AI agents to query and modify RDF data, list named graphs, and access pre-built SPARQL templates on a Fuseki server, with optional HTTP Basic authentication.

The first Model Context Protocol server for Unity projects. It gives AI assistants real understanding of Unity scenes, prefabs, and C# scripts through a queryable knowledge graph.

Provides persistent memory, calendar integration, task management, and proactive assistance for Alexa+ through the Model Context Protocol.

Provides access to personal/team knowledge repositories with tools to search, retrieve, and save information from various sources including Slack, Gmail, and document storage platforms.

Consolidates code understanding, documentation, browser automation, memory, and knowledge graph into a single MCP server with progressive discovery for up to 98% token reduction.

MCP server with persistent memory, voice understanding, multi-thread orchestration, and remote control via Telegram for AI assistants.

A lightweight, powerful local memory server for AI agents supporting text, entities, and relations. Enables persistent codebase understanding and user preference management.

MCP server for semantic code search and dependency graph analysis. Indexes codebases into a knowledge graph with vector embeddings for AI-powered code understanding.

This advanced memory server facilitates neural memory-based sequence learning and prediction, enhancing code generation and understanding through state maintenance and manifold optimization as inspired by Google Research's framework.

Provides a semantic memory layer that integrates LLMs with OpenSearch, enabling storage and retrieval of memories within the OpenSearch engine.

Generates structured project memory and exposes MCP tools for AI coding agents to query topology, read relevant files, and log changes in a repository.

Reduces token waste by caching code understanding in .mi context files and prevents agent conflicts by coordinating intent and file access across sessions.

Enables accessing and managing personal/team internal knowledge repository with tools for semantic search, smart search, document listing, and saving information for future recall.

A robust MCP server that transforms OneNote notebooks into an AI-accessible knowledge base for Gemini Spark, enabling natural language queries to list, read, and search notes via Microsoft Graph API.