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Genesys Cloud MCP Server

Genesys Cloud MCP Server

by Roshan1491
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

conversation_transcript

Retrieves a structured transcript for a given conversation ID, including speaker labels, timestamps, and sentiment annotations, formatted as a time-aligned list of utterances.

Instructions

Retrieves a structured transcript of the conversation, including speaker labels, utterance timestamps, and sentiment annotations where available. The transcript is formatted as a time-aligned list of utterances attributed to each participant (e.g., customer or agent)

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "conversationId": "<conversationId>"
    },
    "name": "conversation_transcript"
  }
}

Input Schema

NameRequiredDescriptionDefault
conversationIdYesThe UUID of the conversation to retrieve the transcript for (e.g., 00000000-0000-0000-0000-000000000000)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.4

TDQS

A3.8/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no readOnlyHint or destructiveHint in annotations, the description carries the behavioral burden; it explicitly says 'Retrieves', which signals a read operation. It also adds important nuance with 'sentiment annotations where available' and clarifies the time-aligned list format, giving the agent a clear expectation of output behavior.

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 two sentences with no filler. The first sentence states the core action and payload, and the second sentence adds formatting detail. Every phrase earns its place.

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

Completeness4/5

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

For a single-parameter retrieval tool with no output schema, the description covers the invocation requirement and the expected return structure well. It could be slightly more complete by noting behavior when no transcript exists or whether transcripts are paginated, but these are minor gaps given the tool's simplicity.

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 already provides 100% coverage for the single parameter, conversationId, including type, format, and an example. The description adds no additional meaning about this parameter, so the baseline score of 3 is appropriate.

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 identifies the action ('Retrieves') and resource ('structured transcript of the conversation'), and enumerates the contents: speaker labels, utterance timestamps, and sentiment annotations. It does not explicitly compare itself to sibling tools like conversation_sentiment or conversation_topics, but the transcript focus is distinct enough for an agent to orient.

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

Usage Guidelines3/5

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

The description implies the tool should be used when a raw, time-aligned conversation transcript is needed. However, it gives no explicit guidance on when to prefer this over sibling tools such as conversation_sentiment, search_voice_conversations, or voice_call_quality, nor does it mention any exclusions or alternatives.

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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