一个利用多个 Claude 实例来提供增强响应的 MCP 服务器。它将相同的请求发送到两个独立的 Claude 实例，并使用第三个实例从两个响应中合并或选择最佳元素。
支持多种 Claude 模型：
克劳德-3-作品-最新
克劳德-3-5-十四行诗-最新
克劳德-3-5-俳句最新
克劳德-3-俳句-20240307
从每个人工智能获得单一、直接的回应
显示原始回复和来源归属
返回优化的最终响应
Related MCP server: prompte-mcp
克隆存储库
安装依赖项：
npm install构建服务器：
npm run build服务器需要 Anthropic API 密钥才能运行。请将其设置为环境变量：
export ANTHROPIC_API_KEY=your-api-key-here服务器提供了一个名为twosplit的工具，其参数如下：
prompt （必填）：发送给 Claude 的提示
model （必需）：要使用的 Claude 模型（必须是上面列出的受支持模型之一）
Claude 中的工具使用示例：
<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>答复将包括：
最终优化响应
两个 AI 的原始回复
来源归属显示哪些部分来自哪个AI
服务器向指定 Claude 模型的两个独立实例发送相同的提示，请求单个直接响应
第三个实例分析了这两个响应，并且：
如果其中一个答案明显更优，则选择最佳答案
创建一个新的响应，结合两个响应中的最佳元素
最终回复、原始回复和来源归属均包含在输出中
要在开发期间以监视模式运行服务器：
npm run watch检查服务器的功能：
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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