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MushroomFleet

TranscriptionTools MCP Server

get_repair_log

Retrieve detailed analysis logs from past repair operations by providing the session ID, enabling efficient review and troubleshooting of transcript repairs.

Instructions

Retrieves detailed analysis log from previous repair operation

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
session_idYesSession ID or timestamp from previous repair

Implementation Reference

  • The main execution function for the 'get_repair_log' tool. It constructs the log file path from the session_id and verifies its existence before returning the path.
    export async function getRepairLog(params: GetRepairLogParams): Promise<{ log_file: string }> {
      try {
        const { session_id } = params;
        const logPath = `/logs/repairs/${session_id}.log`;
        
        // Check if the log file exists
        try {
          await FileHandler.readTextFile(logPath);
        } catch (error) {
          throw new Error(`Repair log not found for session ${session_id}`);
        }
        
        return { log_file: logPath };
      } catch (error) {
        throw new Error(`Failed to retrieve repair log: ${error instanceof Error ? error.message : String(error)}`);
      }
    }
  • TypeScript interface defining the input parameters (session_id) for the get_repair_log tool.
    export interface GetRepairLogParams {
      session_id: string;
    }
  • src/index.ts:74-87 (registration)
    Registration of the 'get_repair_log' tool in the ListToolsRequestSchema response, including input JSON schema.
    {
      name: 'get_repair_log',
      description: 'Retrieves detailed analysis log from previous repair operation',
      inputSchema: {
        type: 'object',
        properties: {
          session_id: {
            type: 'string',
            description: 'Session ID or timestamp from previous repair'
          }
        },
        required: ['session_id']
      }
    },
  • src/index.ts:170-183 (registration)
    Dispatch handler in CallToolRequestSchema that validates input and invokes the getRepairLog function.
    case 'get_repair_log':
      // Validate required parameters
      if (!args || typeof args.session_id !== 'string') {
        throw new McpError(ErrorCode.InvalidParams, 'Missing required parameter: session_id');
      }
      const logResult = await getRepairLog(args as unknown as GetRepairLogParams);
      return {
        content: [
          {
            type: 'text',
            text: JSON.stringify(logResult, null, 2)
          }
        ]
      };
Behavior2/5

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 retrieves a log, implying a read-only operation, but doesn't disclose critical traits such as whether it requires specific permissions, how data is returned (e.g., format, pagination), error handling, or any rate limits. This is a significant gap for a tool with no annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

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

The description is a single, efficient sentence that front-loads the core purpose ('Retrieves detailed analysis log') with no wasted words. It's appropriately sized for a simple tool with one parameter, making it easy for an agent to parse quickly.

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 tool's complexity (low, with one parameter) but lack of annotations and output schema, the description is incomplete. It doesn't explain what the retrieved log contains, its format, or any behavioral aspects, leaving the agent with insufficient context to use the tool effectively beyond basic parameter input.

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, fully documenting the single parameter 'session_id' as 'Session ID or timestamp from previous repair'. 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.

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 ('Retrieves') and resource ('detailed analysis log from previous repair operation'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'format_transcript' or 'repair_text', which might also involve repair-related operations, so it doesn't reach the highest score.

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 doesn't mention prerequisites (e.g., that a repair operation must have occurred), exclusions, or how it relates to sibling tools like 'repair_text' or 'summary_text', leaving the agent to infer usage context.

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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