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MakingChatbots

Genesys Cloud MCP Server

by MakingChatbots
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
TypeScript
Remote

voice_call_quality

Evaluate voice call audio quality by retrieving the minimum Mean Opinion Score (MOS) for each conversation. Identify poor, acceptable, or excellent quality based on jitter, latency, packet loss, and codec.

Instructions

Retrieves voice call quality metrics for one or more conversations by ID. This tool specifically focuses on voice interactions and returns the minimum Mean Opinion Score (MOS) observed in each conversation as structured JSON. MOS is a measure of perceived audio quality based on factors such as jitter, latency, packet loss, and codec. Use the following legend to interpret MOS values:

• Poor: MOS < 3.5 • Acceptable: 3.5 ≤ MOS < 4.3 • Excellent: MOS ≥ 4.3

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
conversationIdsYesA list of up to 100 conversation IDs to evaluate voice call quality for

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.0.4
    • removedInput schema / additionalProperties
      Removed value: -false
    • addedInput schema / properties / conversationIds / items / pattern
      Added value: +"^([0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[1-8][0-9a-fA-F]{3}-[89abAB][0-9a-fA-F]{3}-[0-9a-fA-F]{12}|00000000-0000-0000-0000-000000000000|ffffffff-ffff-ffff-ffff-ffffffffffff)$"
  2. First observedv1.0.0

TDQS

A3.9/5.0
Behavior4/5

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

With no readOnlyHint or destructiveHint annotations, the description carries the burden of behavioral disclosure. It conveys a read-only operation via 'Retrieves,' states the output format as structured JSON, and provides a detailed MOS legend for interpreting results. It does not cover error handling or authentication, but the core behavior is transparent.

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 front-loaded with the primary action, then provides a clear MOS definition and legend. Each sentence earns its place; the legend is slightly long but necessary for interpreting the output.

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 simple single-parameter tool with no output schema, the description covers input, output, and interpretation. It lacks an explicit response shape and behavior for invalid or non-voice conversations, but these are not critical 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 documents conversationIds thoroughly (UUID format, min/max items), so schema coverage is 100%. The description adds that these IDs are evaluated for voice call quality and that the min MOS is returned, but this is only marginal value 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 clearly states the verb and resource: 'Retrieves voice call quality metrics for one or more conversations by ID.' It distinguishes itself from siblings by emphasizing it focuses specifically on voice interactions and returns MOS scores, unlike sentiment or topic tools.

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 when to use the tool: when you need voice call quality metrics for known conversation IDs. However, it does not explicitly mention alternatives such as search_voice_conversations for finding IDs, nor does it state when not to use this tool.

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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