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Genesys Cloud MCP Server

Genesys Cloud MCP Server

by Roshan1491
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
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query_queue_volumes

Retrieve conversation counts by queue for a specified date range, enabling direct workload comparison across up to 300 queues.

Instructions

Returns a breakdown of how many conversations occurred in each specified queue between two dates. Useful for comparing workload across queues. MAX 300 queue IDs.

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": {
      "queueIds": "<queueIds>",
      "startDate": "<startDate>",
      "endDate": "<endDate>"
    },
    "name": "query_queue_volumes"
  }
}

Input Schema

NameRequiredDescriptionDefault
endDateYesThe end date/time in ISO-8601 format (e.g., '2024-01-07T23:59:59Z')
queueIdsYesList of up to MAX of 300 queue IDs
startDateYesThe start date/time in ISO-8601 format (e.g., '2024-01-01T00:00:00Z')

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.4

TDQS

A3.7/5.0
Behavior3/5

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 does indicate a read-only reporting action ('Returns a breakdown') and states the MAX 300 queue IDs limit, which is helpful. However, it does not disclose date boundary semantics, how missing/invalid queues are handled, or any other operational behavior beyond the schema.

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 concise sentences with no wasted words. The core purpose is front-loaded, followed by a practical use case and the key constraint. Every sentence contributes value.

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?

The tool has no output schema and no safety annotations, so the description must carry more explanatory weight. It explains what is returned and why to use it, but it does not specify the exact return shape, how dates are interpreted at boundaries, or behavior for invalid queue IDs. Adequate for a simple report tool, but with clear gaps.

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 description coverage is 100%, so the schema already documents queueIds, startDate, and endDate. The description adds only the concepts of 'between two dates' and 'per specified queue,' which reinforces the schema but does not provide significant new parameter-level meaning.

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 action ('Returns a breakdown'), the resource (queue volumes/conversations), and the scope ('each specified queue between two dates'). This distinguishes it from siblings like sample_conversations_by_queue, which would return sample content rather than volume counts.

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 gives a use case: 'Useful for comparing workload across queues.' However, it does not explicitly say when not to use this tool or mention alternatives such as sample_conversations_by_queue. Usage context is implied but not fully developed.

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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