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Genesys Cloud MCP Server

Genesys Cloud MCP Server

by Roshan1491
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

conversation_sentiment

Get sentiment analysis scores for any Genesys Cloud conversation. Returns numeric score from -100 to 100 and a positive, neutral, or negative label based on customer phrases.

Instructions

Retrieves sentiment analysis scores for one or more conversations. Sentiment is evaluated based on customer phrases, categorized as positive, neutral, or negative. The result includes both a numeric sentiment score (-100 to 100) and an interpreted sentiment label.

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": {
      "conversationIds": "<conversationIds>"
    },
    "name": "conversation_sentiment"
  }
}

Input Schema

NameRequiredDescriptionDefault
conversationIdsYesA list of up to 100 conversation IDs to retrieve sentiment for

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.4

TDQS

A3.5/5.0
Behavior3/5

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

The description adds useful behavior: sentiment is evaluated on customer phrases, categorized as positive/neutral/negative, and returns a -100 to 100 score plus a label. However, it does not clarify whether the result is a per-conversation mapping or a single aggregate score when multiple IDs are supplied, and it omits failure or auth context. Annotations contain only a title, so the description carries the transparency burden.

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?

Three focused sentences: the first states the core action, the second explains how sentiment is determined, and the third describes the output. No filler or redundant restating of schema details.

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

Completeness3/5

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

For a simple 1-parameter tool with no output schema, the description covers the input and the general nature of the return value. Still, it leaves the output structure ambiguous for batched requests and gives no usage direction relative to nearby analysis tools, so it is not fully complete.

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?

Schema coverage is 100%: the single conversationIds parameter is fully described with format, minItems, maxItems, and example. The description only restates 'one or more conversations,' adding no meaningful semantic detail beyond the schema.

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

Purpose5/5

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

The description opens with a specific verb and resource: 'Retrieves sentiment analysis scores for one or more conversations.' This clearly distinguishes it from sibling tools like conversation_topics and conversation_transcript, which serve different analysis purposes.

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?

There is no guidance about when to use this tool versus alternatives such as conversation_topics or conversation_transcript. The usage is only implied by the tool's purpose, with no explicit conditions, exclusions, or recommendations.

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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