Webex

Enables AI assistants to send messages to Webex spaces and direct conversations through the Model Context Protocol, translating natural language intents into Webex API calls. The MCP server acts as a bridge between LLM tool-use requests and Webex's REST API, handling authentication via bearer tokens and message formatting for both plain text and markdown content.

Allows AI assistants to fetch and read messages from Webex spaces and direct conversations through MCP, enabling context-aware responses based on conversation history. The server queries Webex's message API with pagination support, returning message metadata (sender, timestamp, content) that LLMs can analyze for context or decision-making.

Provides AI assistants with the ability to list, create, and manage Webex spaces and room memberships through MCP tool calls. The server translates LLM intents into Webex API operations for space CRUD, member addition/removal, and space metadata queries, with schema validation for space properties like title and description.

The MCP server implements the Model Context Protocol specification to translate between LLM tool-use requests and Webex API calls, including schema validation, error handling, and response formatting. It uses MCP's tool and resource definitions to expose Webex capabilities with typed parameters, ensuring that LLM-generated requests conform to Webex API requirements before execution.

Handles Webex API authentication by accepting bearer tokens and managing their lifecycle within the MCP server context. The server validates tokens, handles authentication errors, and provides clear error messages when tokens are invalid or lack required scopes, without exposing token details in logs or responses.

Enables AI assistants to upload and reference files in Webex messages through MCP, translating file paths or URLs into Webex-compatible attachments. The server handles file type validation, size limits, and Webex's file upload API, allowing LLMs to attach documents, images, or other media to messages without manual file handling.

Provides AI assistants with the ability to search for and retrieve Webex user information (email, display name, user ID) through MCP, enabling context-aware addressing of messages and membership operations. The server queries Webex's people API with optional filters, returning user metadata that LLMs can use to identify recipients or validate user existence.