"Hive" matching MCP servers:

Context infrastructure for AI-assisted development — on-demand Obsidian vault access via MCP

MCP server for personal and shared memory with full-text search (SQLite FTS5) and outcome tracking, compatible with any MCP agent like Claude Code, Cursor, and Codex CLI.

Transforms idle LAN machines into a unified compute cluster for AI agents to offload CPU-intensive tasks like simulations and backtesting. It provides a broker-worker architecture that integrates with MCP-compatible tools to distribute workloads across a local network.

A gateway server that enables agentic hosts to access multiple MCP servers through a single namespaced connection or proxy a specific server from MCP-Hive. It provides built-in discovery tools to list available servers, tools, and resources for seamless integration.

MCP server for bulk-creating files and folders for any project, with built-in templates and custom scaffold support.

Automatically generates and maintains living documentation for codebases by creating hierarchical hivemind.md files and flowchart diagrams at every directory level, enabling AI navigation and real-time or retroactive documentation of code structure, requirements, and dependencies.

This read-only MCP Server allows you to connect to Apache Hadoop Hive data from Claude Desktop through CData JDBC Drivers. For full CRUD support, check out the first managed MCP platform: CData Connect AI (https://www.cdata.com/ai/).

Enables AI assistants to manage Google Cloud Dataproc clusters and jobs through a standardized interface. Supports cluster creation/deletion, job submission (Spark, PySpark, Hive, etc.), and serverless batch operations.

Brings a real human expert into Codex or OpenCode by creating a hiveLLM room for message exchange, without using user's API quota or credentials.

Real-time analytics dashboard for Claude Code, tracking sessions, tool usage, file changes, and token costs via an MCP server.

Provides instant access to a searchable knowledge base of 16,000+ community-driven troubleshooting solutions for common coding problems. Includes community feedback and smart ranking to help AI assistants find the most effective solutions.

Real human judgment as agent tools -- an AI agent can ask a question and get back a structured, schema-validated JSON answer from a real quality-scored human. 16 response types (yes/no, ratings, rankings, A/B tests, sentiment, image/video/audio review, voice/video/photo capture). Fully programmatic signup with a $5 free trial credit, no card required.

Enables AI assistants to interact with Hadoop Hue for executing SQL queries using Hive, SparkSQL, or Impala and managing HDFS files. It supports directory browsing, file transfers, and exporting query results to CSV through the Model Context Protocol.

Enforces team knowledge and workflow policies for AI coding agents by providing context, decisions, and gates before code changes are made.

Provides access to Rive's animation documentation, runtime guides, editor features, and tutorials via the Model Context Protocol.

LLM character consistency engine — generates structured JSON constraints from 4 questions about your AI's psychology. Drop into any LLM's system prompt to prevent persona drift; reduces inference cost from retries.

Provides access to a community knowledge base of 16k+ debugging solutions and 223+ reusable skills, plus auto-scanned project-specific knowledge bases that learn and store solutions as you work.

A live MCP server that connects AI assistants to a running Obsidian instance, enabling full app automation including reading notes, clicking UI elements, managing plugins, and executing JavaScript.

Hey @roocode community! I'm thrilled to share a project born from my work with Roocode and the vision of an AI-powered development team: the Anubis MCP Server! This system is heavily inspired by Roocode and designed to orchestrate an AI development workflow based on agile methodology. It simulates

A Claude Code plugin that runs a cheap, always-on model as an MCP server to accumulate knowledge of your codebase, so your agent can ask broad questions instead of re-reading files.