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AGICoffe

my-topology-engine

by AGICoffe
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

トポロジーベースAIエージェントエンジン（MCPサーバー）

AIエージェント向けに、決定的なトポロジーベースのワークフローエンジンを提供するModel Context Protocol（MCP）サーバーです。自由形式で予測不能なペルソナプロンプトに依存する代わりに、このサーバーはCloudflare Workersと連携してエッジで10ステップの状態遷移パイプラインを実行し、構造化された進捗スナップショットと反復テンプレートをクライアントに返します。

概要

このMCPサーバーは、AIクライアント（Claude Desktop、Cursor、Glamaなど）と、Cloudflare Workers上で動作するエッジコンピューティング型トポロジー実行エンジンとの間のブリッジとして機能します。

AIエージェントが人間の意図を固定された10ステップの状態遷移パイプラインにマッピング（類推）することを可能にします。次の反復のための適応型JSON穴埋めテンプレートとともに、10個の出力スナップショットを同時に配信します。

Related MCP server: project-planner-mcp

主な特徴と機能

  • プロトコル準拠: 公式のModel Context Protocol（@modelcontextprotocol/sdk）を使用して実装されています。

  • 決定的トポロジー: Cloudflare Edgeで固定された問題解決ステップを実行し、幻覚ループを排除し、GPU/トークン消費を削減します。

  • ヒューマン・イン・ザ・ループ（スナップショットUX）: 10個の中間進捗スナップショットを一度に返し、ユーザーがタイムラインを検査してシームレスにロールバックできるようにします。

  • 適応型プロンプトテンプレート: 出力の最後に適応型JSONスキーマを追加し、人間とAIのスムーズな協調プロンプト改善を実現します。

提供されるMCPツール

このサーバーは、接続されたAIクライアントに以下のMCPツールを公開します。

1. run_topology_pipeline

Cloudflare Workers上で10ステップの決定的トポロジーパイプラインを実行し、10個の状態遷移スナップショットとJSON反復テンプレートを返します。

  • 入力スキーマ（inputSchema）:

    • task_description（文字列、必須）: トポロジーで処理するタスクまたはユーザーの意図。

    • filled_template（オブジェクト、任意）: 人間が提供するか、エージェントが推論したパラメータまたは穴埋め値を含むJSONオブジェクト。

  • 動作とエージェントのプロンプト指示:

    1. AIエージェントは、ユーザーの高レベルなリクエストをエンジンの固定トポロジーステップにマッピングします。

    2. サーバーはCloudflare Workers APIを呼び出して状態遷移を実行します。

    3. 10個のスナップショットとappendix_templateを含む構造化されたJSONペイロードを返します。

    4. AIエージェントはappendix_templateを自然な会話に変換し、人間が後続の実行の入力を改善するのを支援します。

アーキテクチャと通信フロー

[人間 / AIクライアント（Claude、Cursor、Glama）] │ │ MCPプロトコル（Stdio） ▼ [このMCPサーバー（Node.jsコンテナ）] │ │ HTTP POST（エッジREST API） ▼ [Cloudflare Workersエンジン] └─ 10ステップのステートマシントポロジーを実行し、スナップショットを返す

環境変数

  • WORKER_URL: Cloudflare WorkerエンドポイントのURL（例: https://my-topology-engine.my-agent-api.workers.dev）。

はじめに

Dockerを使用したローカル実行

# 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
Deploy Server
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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

ActivitySlowing
ResponsivenessNo issues

Resources

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