Ein MCP-Server, der mehrere Claude-Instanzen nutzt, um erweiterte Antworten bereitzustellen. Er sendet dieselbe Eingabeaufforderung an zwei separate Claude-Instanzen und verwendet eine dritte Instanz, um die besten Elemente aus beiden Antworten zu kombinieren oder auszuwählen.
Unterstützt mehrere Claude-Modelle:
Claude-3-Opus-Neueste
Claude-3-5-Sonett-Neueste
Claude-3-5-Haiku-Neueste
claude-3-haiku-20240307
Erhält einzelne, direkte Antworten von jeder KI
Zeigt Originalantworten und Quellenangaben
Gibt optimierte Endantwort zurück
Related MCP server: prompte-mcp
Klonen Sie das Repository
Installieren Sie Abhängigkeiten:
npm installErstellen Sie den Server:
npm run buildDer Server benötigt einen Anthropic API-Schlüssel, um zu funktionieren. Legen Sie ihn als Umgebungsvariable fest:
export ANTHROPIC_API_KEY=your-api-key-hereDer Server stellt ein einzelnes Tool namens twosplit mit den folgenden Parametern bereit:
prompt (erforderlich): Die Eingabeaufforderung zum Senden an Claude
model (erforderlich): Das zu verwendende Claude-Modell (muss eines der oben aufgeführten unterstützten Modelle sein)
Beispiel für die Verwendung von Werkzeugen in Claude:
<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>Die Antwort enthält:
Die endgültige optimierte Antwort
Originalantworten beider KIs
Quellenangabe, die zeigt, welche Teile von welcher KI stammen
Der Server sendet die gleiche Eingabeaufforderung an zwei separate Instanzen des angegebenen Claude-Modells und fordert eine einzige direkte Antwort an
Eine dritte Instanz analysiert beide Antworten und führt entweder Folgendes aus:
Wählt die beste Einzelantwort aus, wenn eine eindeutig überlegen ist
Erstellt eine neue Antwort, die die besten Elemente aus beiden Antworten kombiniert
Die endgültige Antwort, die ursprünglichen Antworten und die Quellenangabe sind alle in der Ausgabe enthalten
So führen Sie den Server während der Entwicklung im Überwachungsmodus aus:
npm run watchSo überprüfen Sie die Fähigkeiten des Servers:
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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