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AGICoffe

my-topology-engine

by AGICoffe
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JavaScript
Remote

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.

Related MCP server: project-planner-mcp

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"
      ]
    }
  }
}

Available Tools

1 tool
execute_topology_engineC

Executes a 10-step deterministic problem-solving topology engine to process tasks and generate state-transition snapshots.

ParametersJSON Schema
NameRequiredDescriptionDefault
filled_templateNoOptional key-value parameters to constrain or guide the 10-step execution.
task_descriptionYesThe primary task or problem to be solved.

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations are absent, so the description carries the full burden. It mentions 'deterministic' and '10-step' but does not disclose what happens during execution, side effects, required permissions, or what the output 'state-transition snapshots' entail. The behavior is underspecified for a tool with no annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, concise sentence with no redundant information. It is front-loaded with the core action and key characteristics. However, it could benefit from a couple more sentences to clarify usage, but as is, it is efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of a 10-step engine and no output schema, the description is insufficient. It does not explain what 'state-transition snapshots' are, how the filled_template constrains execution, or what the tool returns. The tool's operation remains abstract.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% for the two parameters, but the description adds minimal meaning beyond the schema. It names 'filled_template' and 'task_description' but does not explain how they influence the 10-step execution. Baseline 3 is appropriate as the schema covers parameter definitions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool executes a 10-step deterministic problem-solving topology engine to process tasks and generate state-transition snapshots. The purpose is specific and distinct, though it introduces jargon ('topology engine') that could be ambiguous without further context.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to use this tool versus alternatives, nor exclusions or prerequisites. The description implies it should be used for tasks requiring a structured problem-solving approach, but it does not explicitly define appropriate use cases or situations to avoid.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool updatev1.0.0
    • First observedexecute_topology_engine
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TDQS

B3.2/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of confusion between different tools. The tool's purpose is clearly described as executing a topology engine, leaving no ambiguity.

Naming Consistency5/5

The single tool name follows a clear verb_noun pattern ('execute_topology_engine'), which is consistent and predictable. Even with one tool, the naming is well-structured and aligns with common conventions.

Tool Count3/5

One tool is on the borderline of being insufficient for a typical server, as most servers require at least a few tools to cover related operations. However, if the server's sole purpose is to run this engine, the count could be justified, but it feels thin without additional context.

Completeness2/5

The server exposes only a single operation, lacking any configuration, query, or retrieval tools that would complement the execution workflow. This creates significant gaps for agents that may need to set up inputs or inspect outputs, making the surface incomplete for a broader task lifecycle.

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources

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