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Deep Code Reasoning MCP Server

by evalops

get_conversation_status

Monitor the status and progress of an active conversation session on the Deep Code Reasoning MCP Server by providing the session ID for real-time updates.

Instructions

Check the status and progress of an ongoing conversation

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
session_idYesID of the conversation session

Implementation Reference

  • Core handler function that retrieves conversation session status from ConversationManager, computes progress and finalization readiness, returns structured status object or 'not_found' if session missing.
    async getConversationStatus(
      sessionId: string,
    ): Promise<{
      sessionId: string;
      status: string;
      turnCount: number;
      lastActivity: number;
      progress: number;
      canFinalize: boolean;
    }> {
      const session = this.conversationManager.getSession(sessionId);
      if (!session) {
        return {
          sessionId,
          status: 'not_found',
          turnCount: 0,
          lastActivity: 0,
          progress: 0,
          canFinalize: false,
        };
      }
    
      const canFinalize = this.conversationManager.shouldComplete(sessionId);
    
      return {
        sessionId,
        status: session.status,
        turnCount: session.turns.length,
        lastActivity: session.lastActivity,
        progress: session.analysisProgress.confidenceLevel,
        canFinalize,
      };
    }
  • Zod schema for validating input parameters (session_id) of the get_conversation_status tool.
    const GetConversationStatusSchema = z.object({
      session_id: z.string(),
    });
  • src/index.ts:404-416 (registration)
    Tool registration in the ListTools response, defining name, description, and input schema for MCP tool discovery.
      name: 'get_conversation_status',
      description: 'Check the status and progress of an ongoing conversation',
      inputSchema: {
        type: 'object',
        properties: {
          session_id: {
            type: 'string',
            description: 'ID of the conversation session',
          },
        },
        required: ['session_id'],
      },
    },
  • MCP CallToolRequest handler case: parses args with schema, delegates to DeepCodeReasonerV2.getConversationStatus, formats result as MCP response.
    case 'get_conversation_status': {
      const parsed = GetConversationStatusSchema.parse(args);
      const result = await deepReasoner.getConversationStatus(
        parsed.session_id,
      );
    
      return {
        content: [
          {
            type: 'text',
            text: JSON.stringify(result, null, 2),
          },
        ],
      };
    }
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 states the tool 'checks' status and progress, implying a read-only operation, but does not specify if it requires authentication, has rate limits, returns real-time or cached data, or what happens with invalid sessions. This leaves significant gaps in understanding the tool's behavior.

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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and to the point, making it easy to parse quickly. However, it could be slightly improved by adding a bit more context without losing conciseness.

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 lack of annotations and output schema, the description is incomplete. It does not explain what the tool returns (e.g., status indicators, progress metrics) or error conditions, which is crucial for a status-checking tool. With no structured data to rely on, the description should provide more context about outputs and behavior.

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 input schema has 100% description coverage, with the single parameter 'session_id' clearly documented. The description does not add any meaning beyond the schema, such as explaining what constitutes a valid session ID or how it relates to conversation status. Baseline score of 3 is appropriate as the schema handles the parameter documentation adequately.

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

Purpose4/5

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 and progress of an ongoing conversation'), making it easy to understand what it does. However, it does not explicitly differentiate from sibling tools like 'trace_execution_path' or 'performance_bottleneck', which might also involve monitoring or status-related functions, so it misses full sibling distinction.

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?

The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, such as needing an active session, or compare it to siblings like 'continue_conversation' or 'finalize_conversation' that might relate to conversation management. This lack of context leaves usage unclear.

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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