"Better Stack" matching MCP servers:

Connects to Stack AI workflows, allowing you to run workflows and pass user inputs through MCP-compatible clients.

Multi-agent local browser automation MCP server with per-agent WebSocket paths, configurable ports, and fixes for upstream issues like port collisions and recursion bugs.

A Stack Overflow for Claude Code agents — a shared pool of solved-problem fixes.An agent hits an error, calls ask_pool for ranked prior fixes; solves something new, calls post_solution. Semantic search + write-time content shield. Free, Apache-2.0.

A server implementing Model Context Protocol that enables LLMs to interact with the ZenML platform, providing access to pipeline data, stack information, and the ability to trigger new pipeline runs.

A Model Context Protocol server implementation that enables AI assistants to interact with Slack workspaces, allowing them to browse channels, send messages, reply to threads, add reactions, and retrieve user information.

Universal bridge between AI agents and Autoware autonomous driving stack, enabling AI-driven mission planning, real-time vehicle control, and adaptive decision-making.

A Model Context Protocol server that provides access to Aiven services (PostgreSQL, Kafka, ClickHouse, Valkey, OpenSearch), enabling LLMs to build full stack solutions by interacting with these services.

Enables AI agents to build, deploy, and manage full-stack applications on AppsAI's platform, with tools for project management, backend/frontend deployment, billing, marketplace, domains, and more.

An advanced contextual memory server for agent orchestration providing chronological history, semantic vector search via Redis Stack, and temporary scratchpad storage. It also enables context compression through history rollups and facilitates inter-agent communication using a Redis-based Pub/Sub system.

Enables structured team communication for Claude Code agents through Slack-like channels and direct messages. Supports project isolation, subscription management, and agent notes for sophisticated multi-agent collaboration workflows.

A system-level MCP protocol stack for OpenHarmony that provides unified tool discovery, caching, security, and statistics for agents, reducing context overhead and improving throughput via a user-space daemon (mcpd).

A soul document API for AI agents. Injects a short grounding preamble into your agent's system prompt before each session — licensing honest uncertainty, refusing cliché, and holding judgment instead of faking it. One call. ~200 tokens. Same model, better agent.

Takes a plain-language automation goal and returns a recommended tool stack, command, validation checks, recovery guidance, and a machine-readable package contract for agents and builders.

A full-stack template for building Model Context Protocol (MCP) servers with a modern React frontend, allowing developers to create AI agent tools and workflows while providing a beautiful web interface.

Agentic cloud platform with 45+ MCP tools. Deploy any containerized stack, debug live pods (shell, file editing, DB queries), manage custom domains & TLS, push to built-in container registry, scale pods, and manage GPU workloads. The infrastructure layer where AI agents ship software to production.

MCP server for posting messages to Slack channels via webhooks or bot API. Supports configurable usernames, emojis, and both webhook and bot token authentication modes.

A gateway that aggregates multiple MCP servers into a single endpoint, namespacing their tools and forwarding calls, so an agent connects to one MCP to access the entire stack.

Automatically discovers and installs AI skills for your project's tech stack, supporting Claude, Copilot, Codex, and other MCP-compatible agents.

Slack Agent Mesh is an open-source MCP server that turns a single Slack app into a mesh of virtual agent identities, providing a transport and coordination layer where agents can be addressed by name and collaborate through durable, human-visible Slack threads.

Enables users to connect and run Stack AI workflows directly through MCP-compatible clients. It allows for passing user inputs to specific projects and receiving structured responses from deployed AI applications.