genesys-mcp-server
A read-only MCP server that exposes Genesys Cloud queue lookup, analytics aggregates, and conversation search tools, enabling natural language queries for contact center metrics.
README
Genesys Cloud MCP Server (read-only)
A minimal MCP server wrapping three read-only Genesys Cloud Platform API calls, meant as the
foundation described in ../Genesys-RCA-Agent-Architecture.md.
Grow this by adding more @mcp.tool() functions in server.py as the RCA agent needs more
signal (agent adherence, IVR flow errors, edge/telephony health, quality evaluations, ...).
Tools included:
list_queues(name_filter)— resolve a queue name to its id.get_queue_aggregates(queue_id, start, end)— SLA%, ASA, abandon %, AHT etc. for a window.search_conversations(queue_id, start, end, limit)— conversation-level detail for a window.
Not tested against a live org. The request bodies for the two analytics endpoints follow Genesys Cloud's documented query shape, but Genesys's exact field/metric names have shifted across API versions before. Before wiring this into anything scheduled, run each tool once through the MCP Inspector (step 4) against your real org and fix up any field names that come back wrong — the error message from
GenesysApiErrorwill include Genesys's own response body, which usually says exactly what's missing/misnamed.
1. Create a read-only OAuth client in Genesys Cloud
- Admin → Integrations → OAuth → Add Client.
- Grant type: Client Credentials.
- Roles: assign (or create) a custom role with only view-level permissions — at minimum:
routing:queue:view, and the relevantanalytics:*:viewpermissions for queue observations, queue aggregates, and conversation details. Do not grant any write/edit permissions — this server has no business needing them, and it removes an entire class of risk if the client secret ever leaks. - Save, then copy the Client ID and Client Secret — the secret is only shown once.
- Note your org's region domain (Admin → Organization Settings, or look at the domain you log
in through) — e.g.
mypurecloud.com,usw2.pure.cloud,mypurecloud.ie.
2. Local setup
Requires Python 3.10+. (Not currently installed on this machine — grab it from
python.org or winget install Python.Python.3.12 if you're
setting this up here; a real deployment would run this on a server/container instead anyway.)
cd genesys-mcp-server
python -m venv .venv
.venv\Scripts\activate
pip install -r requirements.txt
copy .env.example .env
Edit .env with the client id/secret/environment from step 1.
3. Sanity-check the Genesys auth call
Before involving MCP at all, confirm the OAuth client works:
python -c "from config import Settings; from genesys_client import GenesysClient; s=Settings.from_env(); c=GenesysClient(s.client_id, s.client_secret, s.environment); print(c.request('GET', '/api/v2/routing/queues', params={'pageSize': 1}))"
If that prints queue JSON, auth and network access are good. If it raises GenesysAuthError,
double check the client id/secret/environment. If it raises a 403 on the queues call, the
OAuth client's role is missing routing:queue:view.
4. Test the MCP server interactively (no Claude needed yet)
The mcp[cli] package ships an Inspector — a local web UI that lets you call each tool by hand
and see the raw response, without wiring up a real MCP client first:
mcp dev server.py
This prints a local URL — open it, and you should see list_queues, get_queue_aggregates, and
search_conversations listed as tools. Run list_queues first to get a real queue_id, then
try get_queue_aggregates with a recent time window and see whether the response shape matches
what the tool's docstring promises. Fix up server.py here before moving on.
5. Connect it to Claude
Claude Desktop — one command registers it:
mcp install server.py --name "Genesys Cloud"
This writes an entry into Claude Desktop's claude_desktop_config.json pointing at this
server's venv Python and server.py, and picks up your .env automatically. Restart Claude
Desktop and the three tools should show up under the 🔌 icon.
Claude Code (this CLI) — add it as a project or user MCP server:
claude mcp add genesys-cloud -- python "C:\Users\g352280\OneDrive - Principal Financial Group\Ai\Claude\genesys-mcp-server\server.py"
(Run that from a real terminal, not this session — Claude Code reads MCP config at startup.)
Or add it by hand to .mcp.json / claude_desktop_config.json:
{
"mcpServers": {
"genesys-cloud": {
"command": "python",
"args": ["C:\\Users\\g352280\\OneDrive - Principal Financial Group\\Ai\\Claude\\genesys-mcp-server\\server.py"]
}
}
}
6. Next steps
- Add the remaining tools from the architecture doc's catalog (agent adherence, IVR flow
execution, edge/telephony health, quality evaluations) the same way — one
@mcp.tool()function per Genesys endpoint, inserver.py. - Move
.envsecrets to a real vault (Azure Key Vault) before this runs anywhere but your laptop. - Once there's more than a couple of tools, consider splitting
server.pyinto one module per Genesys API area (analytics, routing, telephony, quality) and importing them into a singleFastMCPinstance.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.
E2B
Using MCP to run code via e2b.