This server combines DeepSeek R1 for reasoning with Claude 3.5 Sonnet for response generation via OpenRouter.
Capabilities:
Generate AI responses with a two-stage process: DeepSeek performs initial reasoning, then Claude generates the final response
Track response status through an asynchronous polling mechanism
Manage conversation context with options to clear history (clearContext) or include Cline history (includeHistory)
Access and optionally view detailed reasoning from DeepSeek (showReasoning parameter)
Leverage Claude's large context window for comprehensive responses
Configure response parameters (temperature, repetition penalty) for optimization
Monitor multiple conversations through automated tracking and filtering
Supports environment configuration through .env files to store API keys and model configuration settings.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Deepseek-Thinking-Claude-3.5-Sonnet-CLINE-MCP Explain quantum computing in simple terms with step-by-step reasoning"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A Model Context Protocol (MCP) server that combines DeepSeek R1's reasoning capabilities with Claude 3.5 Sonnet's response generation through OpenRouter. This implementation uses a two-stage process where DeepSeek provides structured reasoning which is then incorporated into Claude's response generation.
Two-Stage Processing:
Uses DeepSeek R1 for initial reasoning (50k character context)
Uses Claude 3.5 Sonnet for final response (600k character context)
Both models accessed through OpenRouter's unified API
Injects DeepSeek's reasoning tokens into Claude's context
Smart Conversation Management:
Detects active conversations using file modification times
Handles multiple concurrent conversations
Filters out ended conversations automatically
Supports context clearing when needed
Optimized Parameters:
Model-specific context limits:
DeepSeek: 50,000 characters for focused reasoning
Claude: 600,000 characters for comprehensive responses
Recommended settings:
temperature: 0.7 for balanced creativity
top_p: 1.0 for full probability distribution
repetition_penalty: 1.0 to prevent repetition
Related MCP server: DeepSeek-Claude MCP Server
To install DeepSeek Thinking with Claude 3.5 Sonnet for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @newideas99/Deepseek-Thinking-Claude-3.5-Sonnet-CLINE-MCP --client claudeClone the repository:
git clone https://github.com/yourusername/Deepseek-Thinking-Claude-3.5-Sonnet-CLINE-MCP.git
cd Deepseek-Thinking-Claude-3.5-Sonnet-CLINE-MCPInstall dependencies:
npm installCreate a .env file with your OpenRouter API key:
# 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 responsesBuild the server:
npm run buildAdd to your Cline MCP settings (usually in ~/.vscode/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json):
{
"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": []
}
}
}The server provides two tools for generating and monitoring responses:
Main tool for generating responses with the following parameters:
{
"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
}Tool for checking the status of a response generation task:
{
"taskId": string // Required: The task ID from generate_response
}The server uses a polling mechanism to handle long-running requests:
Initial Request:
generate_response returns immediately with a task ID
Response format: {"taskId": "uuid-here"}
Status Checking:
Use check_response_status to poll the task status
Note: Responses can take up to 60 seconds to complete
Status progresses through: pending → reasoning → responding → complete
Example usage in Cline:
// 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
}For development with auto-rebuild:
npm run watchReasoning Stage (DeepSeek R1):
Uses OpenRouter's reasoning tokens feature
Prompt is modified to output 'done' while capturing reasoning
Reasoning is extracted from response metadata
Response Stage (Claude 3.5 Sonnet):
Receives the original prompt and DeepSeek's reasoning
Generates final response incorporating the reasoning
Maintains conversation context and history
MIT License - See LICENSE file for details.
Based on the RAT (Retrieval Augmented Thinking) concept by Skirano, which enhances AI responses through structured reasoning and knowledge retrieval.
This implementation specifically combines DeepSeek R1's reasoning capabilities with Claude 3.5 Sonnet's response generation through OpenRouter's unified API.
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.
check_response_statusgenerate_responseScored across 2 tools
The two tools have clearly distinct purposes: one checks the status of a task, while the other initiates the task itself. There is no overlap or ambiguity between monitoring and execution functions.
Both tools follow a consistent verb_noun pattern (check_response_status, generate_response) with clear action-oriented names. The naming is uniform and predictable across the set.
With only 2 tools, the server feels thin for its apparent scope of AI response generation with reasoning and status tracking. This minimal set may force agents to work around missing operations like error handling or configuration adjustments.
The toolset is severely incomplete for a response generation service. It lacks essential operations such as canceling tasks, retrieving task history, configuring generation parameters, or handling errors, which are typical in such AI workflow domains.
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