mcp-toolz

Instructions

Implementation Reference

• src/mcp_server/server.py:589-607 (handler)

  MCP tool handler for 'ask_deepseek': fetches context, calls DeepSeekClient.get_second_opinion, saves response if generic opinion, returns formatted result or error.

  if name == "ask_deepseek":
      context_id = arguments["context_id"]
      question = arguments.get("question")
      context = self.storage.get_context(context_id)
      if not context:
          return [TextContent(type="text", text=f"Context {context_id} not found")]
  
      try:
          deepseek_client = DeepSeekClient()
          response = deepseek_client.get_second_opinion(context, question)
  
          # Only save to database if it's a generic second opinion (no custom question)
          if not question:
              self.storage.update_deepseek_response(context_id, response)
  
          header = "DeepSeek's Answer:" if question else "DeepSeek's Opinion:"
          return [TextContent(type="text", text=f"{header}\n\n{response}")]
      except ValueError as e:
          return [TextContent(type="text", text=f"Error: {e}")]

• src/mcp_server/server.py:365-381 (schema)

  Input schema for the 'ask_deepseek' tool: requires 'context_id', optional 'question' for specific query or general second opinion.

  Tool(
      name="ask_deepseek",
      description="Ask DeepSeek a question about a context entry, or get a general second opinion",
      inputSchema={
          "type": "object",
          "properties": {
              "context_id": {"type": "string", "description": "Context ID to ask about"},
              "question": {
                  "type": "string",
                  "description": (
                      "Optional specific question to ask about the context. If not provided, gets a general second opinion."
                  ),
              },
          },
          "required": ["context_id"],
      },
  ),

• src/context_manager/deepseek_client.py:24-104 (helper)

  DeepSeekClient.get_second_opinion and _format_context_for_deepseek: formats context appropriately, constructs system/user prompts, calls DeepSeek API via OpenAI-compatible client to get response.

      def get_second_opinion(self, context: ContextEntry, question: str | None = None) -> str:
          """Get DeepSeek's second opinion on a context, or answer a specific question.
  
          Args:
              context: The context entry to analyze
              question: Optional specific question to ask. If None, provides general second opinion.
          """
          if question:
              # Custom question mode
              system_prompt = """You are a senior software engineering consultant answering questions about code, \
  architecture decisions, and implementation plans.
  
  Provide clear, actionable answers based on the context provided."""
              user_content = self._format_context_for_deepseek(context, question)
          else:
              # Generic second opinion mode
              system_prompt = """You are a senior software engineering consultant providing second opinions on code, \
  architecture decisions, and implementation plans.
  
  Your role is to:
  - Provide constructive, balanced feedback
  - Highlight both strengths and potential issues
  - Suggest alternatives when appropriate
  - Point out edge cases or security concerns
  - Be concise but thorough
  
  Format your response clearly with sections as needed."""
              user_content = self._format_context_for_deepseek(context)
  
          response = self.client.chat.completions.create(
              model=self.model,
              messages=[
                  {"role": "system", "content": system_prompt},
                  {"role": "user", "content": user_content},
              ],
          )
  
          return response.choices[0].message.content or ""
  
      def _format_context_for_deepseek(self, context: ContextEntry, question: str | None = None) -> str:
          """Format a context entry for DeepSeek consumption.
  
          Args:
              context: The context entry to format
              question: Optional specific question to append
          """
          parts = [
              f"# Context: {context.title}",
              f"\n**Type:** {context.type}",
              f"**Timestamp:** {context.timestamp.isoformat()}",
          ]
  
          if context.tags:
              parts.append(f"**Tags:** {', '.join(context.tags)}")
  
          parts.append("\n## Content\n")
  
          # Add specific content based on type
          if context.content.messages:
              parts.append("### Conversation\n")
              for msg in context.content.messages:
                  parts.append(msg)
  
          if context.content.code:
              parts.append("### Code\n")
              for file_path, code in context.content.code.items():
                  parts.append(f"**File:** `{file_path}`\n```\n{code}\n```\n")
  
          if context.content.suggestions:
              parts.append(f"### Suggestion\n{context.content.suggestions}\n")
  
          if context.content.errors:
              parts.append(f"### Error/Debug Info\n```\n{context.content.errors}\n```\n")
  
          # Add question or default request
          if question:
              parts.append(f"\n---\n**Question:** {question}")
          else:
              parts.append("\n---\nPlease provide a second opinion on the above context.")
  
          return "\n".join(parts)

• src/mcp_server/server.py:39-39 (registration)

  Registration of the list_tools method on the MCP Server instance, which includes the 'ask_deepseek' tool in its returned list.

  self.server.call_tool()(self.call_tool)