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evalops

Deep Code Reasoning MCP Server

by evalops

finalize_conversation

End a conversation session and retrieve a tailored summary in formats like detailed, concise, or actionable, aiding in final code analysis and decision-making.

Instructions

Complete the conversation and get final analysis results

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
session_idYesID of the conversation session
summary_formatNoFormat for the final summary

Implementation Reference

  • Zod schema definition for validating the input parameters of the finalize_conversation tool (session_id required, summary_format optional).
    const FinalizeConversationSchema = z.object({
      session_id: z.string(),
      summary_format: z.enum(['detailed', 'concise', 'actionable']).optional(),
    });
  • src/index.ts:384-402 (registration)
    Registration of the finalize_conversation tool in the MCP server's list of tools, including name, description, and input schema.
    {
      name: 'finalize_conversation',
      description: 'Complete the conversation and get final analysis results',
      inputSchema: {
        type: 'object',
        properties: {
          session_id: {
            type: 'string',
            description: 'ID of the conversation session',
          },
          summary_format: {
            type: 'string',
            enum: ['detailed', 'concise', 'actionable'],
            description: 'Format for the final summary',
          },
        },
        required: ['session_id'],
      },
    },
  • MCP server request handler for call_tool requests: parses args with schema, delegates to DeepCodeReasonerV2.finalizeConversation, returns result as text content.
    case 'finalize_conversation': {
      const parsed = FinalizeConversationSchema.parse(args);
      const result = await deepReasoner.finalizeConversation(
        parsed.session_id,
        parsed.summary_format,
      );
    
      return {
        content: [
          {
            type: 'text',
            text: JSON.stringify(result, null, 2),
          },
        ],
      };
    }
  • Intermediate handler in DeepCodeReasonerV2: acquires conversation lock, validates session, delegates to ConversationalGeminiService.finalizeConversation, merges results with conversation metadata.
    async finalizeConversation(
      sessionId: string,
      summaryFormat?: 'detailed' | 'concise' | 'actionable',
    ): Promise<DeepAnalysisResult> {
      // Acquire lock before processing
      const lockAcquired = this.conversationManager.acquireLock(sessionId);
      if (!lockAcquired) {
        throw new ConversationLockedError(sessionId);
      }
    
      try {
        // Validate session
        const session = this.conversationManager.getSession(sessionId);
        if (!session) {
          throw new SessionNotFoundError(sessionId);
        }
    
        // Get final analysis from Gemini
        const result = await this.conversationalGemini.finalizeConversation(
          sessionId,
          summaryFormat || 'detailed',
        );
    
        // Extract additional insights from conversation manager
        const conversationResults = this.conversationManager.extractResults(sessionId);
    
        // Merge results
        return {
          ...result,
          metadata: {
            ...result.metadata,
            ...conversationResults.metadata,
          },
        };
      } catch (error) {
        console.error('Failed to finalize conversation:', error);
        throw error;
      } finally {
        // Always release lock
        this.conversationManager.releaseLock(sessionId);
      }
    }
  • Core implementation: retrieves Gemini chat session, sends synthesis prompt for final summary based on conversation history, parses response into structured DeepAnalysisResult, cleans up session.
    async finalizeConversation(
      sessionId: string,
      summaryFormat: 'detailed' | 'concise' | 'actionable' = 'detailed',
    ): Promise<DeepAnalysisResult> {
      const chat = this.activeSessions.get(sessionId);
      const context = this.sessionContexts.get(sessionId);
    
      if (!chat || !context) {
        throw new SessionNotFoundError(sessionId);
      }
    
      // Request final synthesis
      const synthesisPrompt = this.buildSynthesisPrompt(summaryFormat);
      const result = await chat.sendMessage(synthesisPrompt);
      const finalAnalysis = result.response.text();
    
      // Parse and structure the final results
      const structuredResult = this.parseConversationalAnalysis(
        finalAnalysis,
        context.claudeContext,
      );
    
      // Clean up session
      this.activeSessions.delete(sessionId);
      this.sessionContexts.delete(sessionId);
    
      return structuredResult;
    }
Behavior2/5

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 'Complete the conversation' and 'get final analysis results', suggesting a read operation that might finalize or close a session, but it doesn't specify if this is destructive, requires permissions, or details the output format. For a tool with no annotations, 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with a single sentence that front-loads the core action. It wastes no words, but could be slightly improved by structuring it to highlight key aspects like the tool's role among siblings. Overall, it's efficient and to the point.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity implied by 'final analysis results' and the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'analysis results' include, how the conversation is 'completed', or any behavioral traits. For a tool that seems to produce outputs, this leaves critical gaps for the agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, with clear descriptions for both parameters in the input schema. The description adds no additional meaning beyond the schema, such as explaining the context of 'session_id' or the implications of 'summary_format' choices. With high schema coverage, the baseline score of 3 is appropriate as the schema handles parameter documentation adequately.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool's purpose with 'Complete the conversation and get final analysis results', which includes a verb ('Complete') and resource ('conversation'), but it's vague about what 'complete' entails and doesn't distinguish it from siblings like 'get_conversation_status' or 'escalate_analysis'. It lacks specificity in how it differs from other conversation-related tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance is provided on when to use this tool versus alternatives. The description implies usage at the end of a conversation, but it doesn't mention prerequisites, exclusions, or compare it to siblings like 'continue_conversation' or 'get_conversation_status'. This leaves the agent without clear direction on tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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