DeepSeek R1の推論機能と、OpenRouterを介したClaude 3.5 Sonnetのレスポンス生成機能を組み合わせたModel Context Protocol（MCP）サーバー。この実装では、DeepSeekが構造化推論を提供し、それがClaudeのレスポンス生成に組み込まれるという2段階のプロセスを採用しています。
2段階処理：
初期推論にDeepSeek R1を使用（5万文字のコンテキスト）
最終回答には Claude 3.5 Sonnet を使用 (60 万文字のコンテキスト)
どちらのモデルもOpenRouterの統合APIを通じてアクセス可能
DeepSeekの推論トークンをClaudeのコンテキストに挿入する
スマート会話管理：
ファイルの変更時刻を使用してアクティブな会話を検出します
複数の同時会話を処理
終了した会話を自動的にフィルタリングします
必要に応じてコンテキストのクリアをサポート
最適化されたパラメータ:
モデル固有のコンテキスト制限:
DeepSeek: 集中的な推論のための50,000文字
クロード：包括的な回答には60万文字
推奨設定:
温度：バランスのとれた創造性のための0.7
top_p: 完全な確率分布の場合は1.0
repetition_penalty: 繰り返しを防ぐには 1.0
Related MCP server: OpenRouter MCP Multimodal Server
Smithery経由で Claude Desktop に DeepSeek Thinking with Claude 3.5 Sonnet を自動的にインストールするには:
npx -y @smithery/cli install @newideas99/Deepseek-Thinking-Claude-3.5-Sonnet-CLINE-MCP --client claudeリポジトリをクローンします。
git clone https://github.com/yourusername/Deepseek-Thinking-Claude-3.5-Sonnet-CLINE-MCP.git
cd Deepseek-Thinking-Claude-3.5-Sonnet-CLINE-MCP依存関係をインストールします:
npm installOpenRouter API キーを使用して.envファイルを作成します。
# Required: OpenRouter API key for both DeepSeek and Claude models
OPENROUTER_API_KEY=your_openrouter_api_key_here
# Optional: Model configuration (defaults shown below)
DEEPSEEK_MODEL=deepseek/deepseek-r1 # DeepSeek model for reasoning
CLAUDE_MODEL=anthropic/claude-3.5-sonnet:beta # Claude model for responsesサーバーを構築します。
npm run buildCline MCP 設定に追加します (通常は~/.vscode/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.jsonにあります)。
{
"mcpServers": {
"deepseek-claude": {
"command": "/path/to/node",
"args": ["/path/to/Deepseek-Thinking-Claude-3.5-Sonnet-CLINE-MCP/build/index.js"],
"env": {
"OPENROUTER_API_KEY": "your_key_here"
},
"disabled": false,
"autoApprove": []
}
}
}サーバーは、応答を生成および監視するための 2 つのツールを提供します。
次のパラメータを使用して応答を生成するためのメイン ツール:
{
"prompt": string, // Required: The question or prompt
"showReasoning"?: boolean, // Optional: Show DeepSeek's reasoning process
"clearContext"?: boolean, // Optional: Clear conversation history
"includeHistory"?: boolean // Optional: Include Cline conversation history
}応答生成タスクのステータスを確認するためのツール:
{
"taskId": string // Required: The task ID from generate_response
}サーバーは、長時間実行されるリクエストを処理するためにポーリング メカニズムを使用します。
最初のリクエスト:
generate_responseタスクIDとともに直ちに返されます
応答形式: {"taskId": "uuid-here"}
ステータスの確認:
check_response_statusを使用してタスクのステータスをポーリングします
**注:**回答には最大 60 秒かかる場合があります
ステータスは、保留中 → 推論中 → 応答中 → 完了の順に進行します。
Cline での使用例:
// Initial request
const result = await use_mcp_tool({
server_name: "deepseek-claude",
tool_name: "generate_response",
arguments: {
prompt: "What is quantum computing?",
showReasoning: true
}
});
// Get taskId from result
const taskId = JSON.parse(result.content[0].text).taskId;
// Poll for status (may need multiple checks over ~60 seconds)
const status = await use_mcp_tool({
server_name: "deepseek-claude",
tool_name: "check_response_status",
arguments: { taskId }
});
// Example status response when complete:
{
"status": "complete",
"reasoning": "...", // If showReasoning was true
"response": "..." // The final response
}自動リビルドを使用した開発の場合:
npm run watch推論段階（DeepSeek R1） ：
OpenRouterの推論トークン機能を使用する
プロンプトは推論をキャプチャしながら「完了」を出力するように変更されました
回答メタデータから推論が抽出される
応答段階（クロード3.5ソネット） ：
元のプロンプトとDeepSeekの推論を受け取る
推論を組み込んだ最終的な回答を生成する
会話のコンテキストと履歴を維持する
MIT ライセンス - 詳細については LICENSE ファイルを参照してください。
構造化推論と知識検索を通じて AI 応答を強化するSkiranoの RAT (検索拡張思考) コンセプトに基づいています。
この実装では、OpenRouter の統合 API を介して、DeepSeek R1 の推論機能と Claude 3.5 Sonnet の応答生成を具体的に組み合わせています。
Check the status of a response generation task
| Name | Required | Description | Default |
|---|---|---|---|
| taskId | Yes | The task ID returned by generate_response |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool checks status but doesn't explain what the status values mean, whether it's read-only or has side effects, or any rate limits or authentication needs. This leaves significant gaps in understanding how the tool behaves beyond its basic function.
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 that directly states the tool's purpose without any wasted words. It is appropriately sized and front-loaded, making it easy to grasp immediately.
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 (checking task status) and the lack of annotations and output schema, the description is incomplete. It doesn't explain what status information is returned, potential outcomes, or error conditions, leaving the agent without enough context to fully understand the tool's behavior and results.
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 the 'taskId' parameter clearly documented as 'The task ID returned by generate_response.' The description adds no additional parameter semantics beyond this, so it meets the baseline score of 3 where 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 with a specific verb ('check') and resource ('status of a response generation task'), making it immediately understandable. However, it doesn't explicitly differentiate from its sibling tool 'generate_response' beyond the implied relationship, which prevents a perfect 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 implies usage context by referencing 'taskId returned by generate_response,' suggesting this tool should be used after initiating a task with its sibling. However, it lacks explicit guidance on when to use it versus alternatives or any prerequisites beyond the task ID, leaving some ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate a response using DeepSeek's reasoning and Claude's response generation through OpenRouter.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | The user's input prompt | |
| showReasoning | No | Whether to include reasoning in response | |
| clearContext | No | Clear conversation history before this request | |
| includeHistory | No | Include Cline conversation history for context |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the AI models involved (DeepSeek and Claude) and the platform (OpenRouter), but doesn't describe key behavioral traits like rate limits, authentication needs, response format, error handling, or whether it's a read/write operation. The description adds some context about the implementation but lacks crucial operational details.
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 that states the core functionality without unnecessary words. It's appropriately sized for the tool's complexity and gets straight to the point. Every word earns its place by specifying both the action and the implementation method.
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 has 4 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what the tool returns, how to interpret results, error conditions, or operational constraints. For a tool that presumably generates AI responses through external services, more context about response format, limitations, and integration details would be needed.
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?
Schema description coverage is 100%, so the schema already fully documents all 4 parameters. The description adds no parameter-specific information beyond what's in the schema. It doesn't explain how parameters interact (e.g., how 'clearContext' and 'includeHistory' relate) or provide usage examples. This meets the baseline of 3 when schema coverage is complete.
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 action ('Generate a response') and specifies the implementation method ('using DeepSeek's reasoning and Claude's response generation through OpenRouter'). It distinguishes from the sibling tool 'check_response_status' by focusing on generation rather than status checking. However, it doesn't specify what type of response is generated (e.g., text completion, analysis, etc.), keeping it at a 4 rather than a perfect 5.
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. It doesn't mention when to use it over other response generation methods or when the sibling tool 'check_response_status' would be appropriate. There's no context about use cases, prerequisites, or limitations, leaving the agent with minimal usage direction.
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.
v1.0.0check_response_statusgenerate_responseScored across 2 tools
The two tools have clearly distinct purposes: one checks the status of a response generation task, while the other initiates the generation of a response. There is no overlap or ambiguity between these functions, making it easy for an agent to select the correct tool.
Both tools follow a consistent verb_noun naming pattern (check_response_status and generate_response), using snake_case throughout. The verbs 'check' and 'generate' appropriately describe their actions, and there are no deviations in style or convention.
With only 2 tools, the server feels thin for its apparent purpose of response generation through OpenRouter. A more complete surface might include tools for managing tasks, handling errors, or configuring parameters, but the current set is minimal and may limit agent workflows.
The tool surface is severely incomplete for response generation tasks. While it covers initiating and checking status, it lacks tools for canceling tasks, retrieving results beyond status, handling errors, or managing task history. This will likely cause agent failures in more complex scenarios.
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