Genesys Cloud MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| GENESYSCLOUD_REGION | Yes | The Genesys Cloud region | |
| GENESYSCLOUD_OAUTHCLIENT_ID | Yes | The OAuth Client ID for authentication | |
| GENESYSCLOUD_OAUTHCLIENT_SECRET | Yes | The OAuth Client Secret for authentication |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| search_queuesA | Searches for routing queues based on their name, allowing for wildcard searches. Returns a paginated list of matching queues, including their Name, ID, Description (if available), and Member Count (if available). Also provides pagination details like current page, page size, total results found, and total pages available. Useful for finding specific queue IDs, checking queue configurations, or listing available queues. |
| sample_conversations_by_queueA | Retrieves conversation analytics for a specific queue between two dates, returning a representative sample of conversation IDs. Useful for reporting, investigation, or summarisation. |
| query_queue_volumesA | 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. |
| voice_call_qualityA | 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 |
| conversation_sentimentA | 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. |
| conversation_topicsA | Retrieves Speech and Text Analytics topics detected for a specific conversation. Topics represent business-level intents (e.g. cancellation, billing enquiry) inferred from recognised phrases in the customer-agent interaction. |
| search_voice_conversationsA | Searches for voice conversations within a specified time window, optionally filtering by phone number. Returns a paginated list of conversation IDs and call duration for use in further analysis or tool calls. |
| conversation_transcriptA | 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) |
| oauth_clientsA | Retrieves a list of all OAuth clients, including their associated roles and divisions. This tool is useful for auditing and managing OAuth clients in the Genesys Cloud organization. |
| oauth_client_usageA | Retrieves the usage of an OAuth Client for a given period. It returns the total number of requests and a breakdown of Platform API endpoints used by the client. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 10 tools
Most tools target clearly distinct outputs: queue search, conversation search, transcripts, sentiment, topics, call quality, and OAuth data. The main ambiguity is between sample_conversations_by_queue and query_queue_volumes, both of which aggregate conversations by queue, though their descriptions do clarify that one returns IDs and the other returns counts.
All names are snake_case and readable, but conventions are mixed: four tools start with action verbs (search_queues, sample_conversations_by_queue, query_queue_volumes, search_voice_conversations) while the remaining six start with resource nouns (conversation_topics, oauth_clients, etc.). The conversation_* group is consistent, but there is no single predictable pattern across the set.
With 10 tools, the set is well within the ideal range and each tool serves a distinct part of the Genesys Cloud surface: queue lookup, conversation search, analytics, transcripts, quality, and OAuth auditing. No tool feels redundant or unnecessary.
The set supports a coherent read/analytics workflow: find queues, search or sample conversations, then retrieve transcripts, topics, sentiment, and call quality for conversation IDs. Gaps such as non-voice conversation search and OAuth client management exist, but they appear to be outside the intended analytics/audit scope.