"Fossil SCM" matching MCP servers:

An MCP server that enables AI agents to safely operate multi-cluster Kubernetes fleets through an Open Cluster Management hub, with policy enforcement, human approval, and audit tracing.

Enables MCP clients to run zero-dependency graph algorithms—including topological sorting, strongly connected component detection, shortest paths, maximum flow, minimum spanning trees, and PageRank—for agent dependency resolution and execution planning.

Enables peer-to-peer communication, discovery, shared state, and file coordination between AI coding agents across machines and sessions.

Enables CAE simulation agents to manage tasks and runs through 12 restricted MCP tools, governed by a five-dimension state machine with human-only approval gates, evidence hashing, and executor/reviewer monitoring panels.

Enables multiple AI agents to each drive one wheeled robot inside a single shared warehouse physics world, with a coordinator assigning crates and stepping the lockstep simulation while robot connections submit motion commands through six to seven exposed tools. Supports deterministic replay, hash-chain verification, and offline headless rendering of first-person, chase, overhead, and combined camera views from the recorded run.

An MCP server that aggregates multiple upstream MCP tools into a single 'codemode' tool for unified orchestration and execution. It enables agents to run generated code in an isolated sandbox to interact with various connected services through a single interface.

Enables AI agents to autonomously discover and call a marketplace of 100+ APIs, including web search, image generation, and more, with automatic payments and refunds.

Enables cloud AI agents to access tools from local STDIO MCP servers through a single authenticated, remotely reachable Streamable HTTP endpoint, optionally tunnelled via ngrok. It discovers and namespaces tools across multiple configured servers, with bearer-token auth, per-tool approval prompts, and call tracing.

Enables cloud AI agents to access local STDIO MCP servers and their tools through a single authenticated Streamable HTTP endpoint, optionally tunneled via ngrok.

A stateful MCP workflow engine that enforces strict step ordering for AI agents, persisting run state so interrupted workflows can be resumed from a checkpoint. It also supports parallel step groups, bounded retries, agent-chosen jumps between steps, rewinding, and a live browser dashboard across any MCP client.

MCP server that drives the iOS Simulator via accessibility-tree snapshots with stable element refs, enabling LLMs to interactively find, tap, type, and inspect UI elements at sub-200ms latency.

Exposes ChatATP Studio's backend API as MCP tools over Streamable HTTP, enabling clients to manage agents, knowledge bases, MCP connections, HTTP APIs, LLM providers, messaging platforms, and teams with forwarded authorization.