複数のClaudeインスタンスを活用して、より高度な応答を提供するMCPサーバー。同じプロンプトを2つのClaudeインスタンスに送信し、3つ目のインスタンスを使用して両方の応答から最適な要素を組み合わせ、または選択します。
複数のClaudeモデルをサポートします:
クロード-3-opus-最新
クロード-3-5-ソネット-最新
クロード-3-5-俳句-最新
クロード-3-俳句-20240307
各AIから単一の直接応答を取得します
オリジナルの回答と出典を表示します
最適化された最終レスポンスを返す
Related MCP server: prompte-mcp
リポジトリをクローンする
依存関係をインストールします:
npm installサーバーを構築します。
npm run buildサーバーが機能するにはAnthropic APIキーが必要です。環境変数として設定してください。
export ANTHROPIC_API_KEY=your-api-key-hereサーバーは、次のパラメータを持つtwosplitと呼ばれる単一のツールを提供します。
prompt （必須）: クロードに送るプロンプト
model (必須): 使用する Claude モデル (上記のサポートされているモデルのいずれかである必要があります)
Claude でのツールの使用例:
<use_mcp_tool>
<server_name>twosplit</server_name>
<tool_name>twosplit</tool_name>
<arguments>
{
"prompt": "Write a short story about a robot learning to paint",
"model": "claude-3-5-sonnet-latest"
}
</arguments>
</use_mcp_tool>回答には次の内容が含まれます:
最終的な最適化された応答
両AIからの元の回答
どの部分がどのAIから来たのかを示すソース属性
サーバーは、指定されたクロードモデルの2つの別々のインスタンスに同じプロンプトを送信し、単一の直接応答を要求します。
3 番目のインスタンスは両方の応答を分析し、次のいずれかを実行します。
明らかに優れている場合、最適な回答を1つ選択します
両方のレスポンスの最良の要素を組み合わせた新しいレスポンスを作成します
最終的な回答、元の回答、およびソースの帰属はすべて出力に含まれます。
開発中にサーバーをウォッチモードで実行するには:
npm run watchサーバーの機能を検査するには:
npm run inspectorGet multiple AI perspectives and combine them into the best response
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | The Claude model to use (claude-3-opus-latest, claude-3-5-sonnet-latest, claude-3-5-haiku-latest, claude-3-haiku-20240307) | |
| prompt | Yes | The prompt to send to the AI |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions getting 'multiple AI perspectives' and combining them, but doesn't specify how many perspectives, what 'combine' entails (e.g., voting, averaging, synthesis), latency, rate limits, or error handling. For a tool that interacts with AI models, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence: 'Get multiple AI perspectives and combine them into the best response.' It's front-loaded with the core purpose, has zero wasted words, and is appropriately sized for the tool's complexity. Every part of the sentence contributes to understanding the tool's function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (involving multiple AI calls and combination logic), lack of annotations, and no output schema, the description is insufficient. It doesn't explain the output format, error conditions, or behavioral details like how perspectives are generated or combined. For a tool with no structured support, the description should provide more context to be complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with clear documentation for both parameters ('model' and 'prompt'). The description doesn't add any meaning beyond what the schema provides—it doesn't explain parameter interactions or usage nuances. Given the high schema coverage, a baseline score of 3 is appropriate, as the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get multiple AI perspectives and combine them into the best response.' It specifies the action ('get' and 'combine') and the resource ('AI perspectives'), making it easy to understand what the tool does. However, since there are no sibling tools, it doesn't need to differentiate from alternatives, so it doesn't reach the highest score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, prerequisites, or specific contexts. It simply states what the tool does without indicating scenarios where it's most appropriate or any limitations. This lack of usage context leaves the agent without direction on optimal application.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0twosplitScored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined as getting multiple AI perspectives and combining them, which is distinct and unambiguous in this single-tool context.
Since there is only one tool, naming consistency is inherently perfect. The tool name 'twosplit' follows a single pattern with no deviations or mixing of conventions to evaluate.
A single tool is generally too few for most server purposes, as it limits functionality and flexibility. For a server named 'Twosplit MCP Server' that aims to provide multiple AI perspectives, one tool feels thin and under-scoped, potentially leaving gaps in operations like configuration or result refinement.
The tool surface is severely incomplete for the implied domain of handling AI perspectives. While the 'twosplit' tool covers the core action, there are obvious gaps such as no tools for managing perspectives, adjusting combination parameters, or retrieving historical results, which could lead to agent failures in more complex workflows.
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