Webex MCP Server for AI Messaging Access

Overview

This project implements an MCP (Model Context Protocol) server that exposes Cisco Webex messaging functionality to AI assistants. The server acts as an intermediary: it authenticates with Webex on behalf of a bot, receives and forwards messages and events, and exposes a stable, secure API surface that language models or assistant processes can call to send messages, read context, and manage conversations. The goal is to let models participate in real-time Webex conversations without embedding long-lived platform credentials into the model runtime.

Using an MCP server simplifies integration for AI teams. Instead of re-implementing Webex authentication, webhook handling, message threading, and rate-limit logic inside model code, you call the MCP endpoints. The server can handle token management, store conversational state, and scale independently from model inference, enabling safer and more maintainable assistant deployments.

Repository: https://github.com/Kashyap-AI-ML-Solutions/webex-messaging-mcp-server

Features

• Secure Webex bot authentication and token management
• Webhook receiver and event forwarder for messages, edits, attachments
• REST endpoints compatible with MCP-style tool integrations
• Conversation/context management (threads, message history snapshots)
• Support for attachments, adaptive cards, and rich content
• Rate-limit handling and retries for outbound Webex calls
• Optional scalable components (Redis or DB-backed session store)
• Docker-enabled for quick deployment and CI/CD-friendly builds
• Example client scripts and minimal documentation for developers

Installation / Configuration

Prerequisites:

• Node.js (>=14) or Docker
• A Webex bot token (create a bot at developer.webex.com)

Clone the repo and install dependencies:

git clone https://github.com/Kashyap-AI-ML-Solutions/webex-messaging-mcp-server.git
cd webex-messaging-mcp-server
# Install with npm (or yarn)
npm install

Run locally:

# set env variables or create a .env file (see example below)
npm start

Run with Docker:

# build and run
docker build -t webex-mcp-server .
docker run -p 8080:8080 --env-file .env webex-mcp-server

Example .env (replace placeholders):

# Webex bot token (Bearer)
WEBEX_BOT_TOKEN=Bearer YOUR_WEBEX_BOT_TOKEN

# MCP server port
PORT=8080

# Secret for signing JWTs/requests (rotate regularly)
MCP_JWT_SECRET=verysecretvalue

# Optional persistence/backing store (Redis example)
REDIS_URL=redis://localhost:6379

# Public URL for webhooks (use ngrok or a hosted URL)
WEBHOOK_PUBLIC_URL=https://example.com

Key configuration environment variables:

After starting the server, register the Webex webhook (some deployments include automated webhook registration). If manual registration is needed, use the Webex REST API to create a webhook pointing to ${WEBHOOK_PUBLIC_URL}/webhook.

Available Resources

• Source code and examples: https://github.com/Kashyap-AI-ML-Solutions/webex-messaging-mcp-server
• Example client scripts (in repository) to demonstrate:
  • Sending a message into a space
  • Fetching conversation history
  • Uploading an attachment and posting to a thread
• Dockerfile and optional Docker Compose for easy deployment
• Basic API reference (see repository README or /docs folder) listing MCP endpoints exposed by the server

Use Cases

1. AI Assistant Responding in Webex Spaces

   • A model receives conversation context from the MCP server (latest messages + metadata), generates a reply, and calls the MCP endpoint to post the reply. The server handles token use, threading, and message formatting.

2. Automatic Meeting Notes and Summaries

   • A background job uses the MCP API to read messages from a meeting space, summarizes discussions, and posts the summary as a message or card. The server manages history retrieval and attachment posting.

3. Moderation and Policy Enforcement

   • An AI moderation model inspects incoming messages (forwarded by the webhook) and instructs the MCP server to redact, notify moderators, or escalate when policy violations are detected.

4. Multi-model Tooling Layer

   • Multiple models (NLP, classification, knowledge retrieval) call the same MCP server, sharing conversation context and state in Redis. This decouples message transport from model logic and scales each component independently.

5. Customer Support Workflows

   • A bot triages incoming support requests, augments messages with user profile or ticket data, and escalates to a human agent by transferring thread context. The MCP server orchestrates the required Webex calls and keeps an audit trail.

Security and Operations Notes

• Keep WEBEX_BOT_TOKEN and MCP_JWT_SECRET private; use vaults or secret managers in production.
• Serve traffic over TLS and validate inbound webhook signatures when available.
• Use Redis or a database for shared state if running multiple server instances.
• Implement backoff and retry logic for Webex rate limits (server includes basic handling).
• Regularly rotate bot tokens and review webhook subscriptions.

For full implementation details, API endpoints, and example clients, consult the repository: https://github.com/Kashyap-AI-ML-Solutions/webex-messaging-mcp-server.