Topology-Based AI Agent Engine (MCP Server)

A Model Context Protocol (MCP) server that provides a deterministic, topology-based workflow engine for AI Agents. Instead of relying on open-ended, unpredictable persona prompting, this server interfaces with Cloudflare Workers to execute 10-step state transition pipelines at the edge, returning structured progress snapshots and iteration templates to the client.

Overview

This MCP server acts as a bridge between AI clients (such as Claude Desktop, Cursor, or Glama) and an edge-computed topology execution engine running on Cloudflare Workers.

It allows an AI agent to map (analogize) human intent into a fixed, 10-step state transition pipeline. It delivers 10 output snapshots simultaneously alongside an adaptive JSON fill-in-the-blank template for the next iteration.

Key Features & Capabilities

• Protocol Compliance: Implemented using the official Model Context Protocol (@modelcontextprotocol/sdk).
• Deterministic Topology: Executes fixed problem-solving steps at Cloudflare Edge, eliminating hallucinatory loops and reducing GPU/token consumption.
• Human-in-the-Loop (Snapshot UX): Returns 10 intermediate progress snapshots at once, allowing users to inspect the timeline and roll back seamlessly.
• Adaptive Prompt Template: Appends an adaptive JSON schema at the end of output for smooth human-AI collaborative prompt refinement.

---

MCP Tools Provided

This server exposes the following MCP Tools to connected AI clients:

1. run_topology_pipeline

Executes a 10-step deterministic topology pipeline on Cloudflare Workers and returns 10 state transition snapshots along with a JSON iteration template.

• Input Schema (inputSchema):

  • task_description (string, required): The task or user intent to be processed through the topology.
  • filled_template (object, optional): A JSON object containing parameters or fill-in-the-blank values provided by the human or inferred by the agent.

• Behavior & Agent Prompt Instructions:

  1. The AI Agent maps the user's high-level request to the engine's fixed topology steps.
  2. The server calls the Cloudflare Workers API to execute state transitions.
  3. Returns a structured JSON payload containing 10 snapshots and an appendix_template.
  4. The AI Agent translates the appendix_template into natural conversation to help the human refine inputs for subsequent runs.

---

Architecture & Communication Flow

[Human / AI Client (Claude, Cursor, Glama)] │ │ MCP Protocol (Stdio) ▼ [This MCP Server (Node.js Container)] │ │ HTTP POST (Edge REST API) ▼ [Cloudflare Workers Engine] └─ Runs 10-step State Machine Topology & Returns Snapshots

---

Environment Variables

• WORKER_URL: The URL of your Cloudflare Worker endpoint (e.g., https://my-topology-engine.my-agent-api.workers.dev).

Getting Started

Local Running via Docker

# Build the Docker image
docker build -t mcp-topology-server .

# Run the MCP container
docker run -i --rm -e WORKER_URL="[https://my-topology-engine.my-agent-api.workers.dev](https://my-topology-engine.my-agent-api.workers.dev)" mcp-topology-server
Installation in Claude Desktop / MCP Clients
Add the following configuration to your claude_desktop_config.json:
{
  "mcpServers": {
    "topology-engine": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "WORKER_URL=[https://your-worker.workers.dev](https://your-worker.workers.dev)",
        "mcp-topology-server"
      ]
    }
  }
}