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Version: 0.1.0

MCP Server Architecture

The MCP (Model Context Protocol) Server is the core component that enables AI assistants and clients to interact with Slack through a standardized protocol. This document details the architecture, components, and implementation of the MCP server.

Overview​

Common Instance Management

The MCP Server uses the common instance management design shared across all server types in this project. For detailed information about factory patterns, singleton management, token handling, and testing support, see the Common Instance Management documentation.

The MCP Server implements the Model Context Protocol specification, providing Slack integration capabilities through a set of registered tools. It supports multiple transport mechanisms and offers a flexible, extensible architecture for Slack API interactions.

Core Components​

Server Instance (server.py)​

The main MCP server implementation using the mcp library:

from mcp.server import Server

server = Server("slack-mcp-server")

Key Responsibilities:

  • Tool registration and management
  • Request routing and processing
  • Response formatting and validation
  • Error handling and logging
  • Transport mechanism management

Tool Registry​

The server registers six primary MCP tools for Slack integration:

1. slack_post_message​

  • Purpose: Send messages to Slack channels
  • Input: Channel ID, message text, optional thread timestamp
  • Output: Message posting confirmation and metadata

2. slack_read_channel_messages​

  • Purpose: Retrieve messages from Slack channels
  • Input: Channel ID, optional count and cursor parameters
  • Output: Array of channel messages with metadata

3. slack_read_thread_messages​

  • Purpose: Retrieve messages from specific thread
  • Input: Channel ID, thread timestamp, optional count
  • Output: Array of thread messages with metadata

4. slack_thread_reply​

  • Purpose: Reply to existing threads
  • Input: Channel ID, thread timestamp, reply text(s)
  • Output: Reply posting confirmation and metadata

5. slack_read_emojis​

  • Purpose: Retrieve workspace emoji information
  • Input: Optional cursor for pagination
  • Output: Array of custom and default emojis

6. slack_add_reactions​

  • Purpose: Add emoji reactions to messages
  • Input: Channel ID, message timestamp, emoji name(s)
  • Output: Reaction addition confirmation

Transport Mechanisms​

The MCP server supports three transport options:

stdio Transport​

# Default transport for command-line usage
async def main():
    async with mcp.server.stdio.stdio_server() as (read_stream, write_stream):
        await server.run(read_stream, write_stream, InitializationOptions())

Characteristics:

  • Standard input/output communication
  • Ideal for subprocess integration
  • Minimal network overhead
  • Synchronous request-response pattern

Server-Sent Events (SSE)​

# SSE transport for web applications
sse_app = server.sse_app(mount_path="/mcp")

Characteristics:

  • HTTP-based event streaming
  • Browser-compatible
  • Real-time bidirectional communication
  • Automatic reconnection support

HTTP Streaming​

# HTTP streaming transport
http_app = server.streamable_http_app()

Characteristics:

  • RESTful HTTP endpoints
  • Streamable responses
  • Compatible with HTTP clients
  • Stateless request handling

Data Flow Architecture​

Configuration and Initialization​

Environment Variables​

  • SLACK_BOT_TOKEN: Slack bot token for API authentication
  • MCP_TRANSPORT: Transport mechanism selection
  • MCP_MOUNT_PATH: Mount path for HTTP transports
  • LOG_LEVEL: Logging verbosity control

CLI Arguments​

slack-mcp-server \
  --transport sse \
  --mount-path /mcp \
  --log-level INFO

Initialization Flow​

  1. Environment Loading: Load configuration from environment variables
  2. Client Creation: Initialize Slack API client with bot token
  3. Tool Registration: Register all MCP tools with the server
  4. Transport Setup: Configure selected transport mechanism
  5. Server Start: Begin listening for MCP requests

Error Handling​

Error Categories​

  • Authentication Errors: Invalid or missing Slack tokens
  • API Errors: Slack API rate limits or service errors
  • Validation Errors: Invalid input parameters
  • Network Errors: Connection issues with Slack API

Error Response Format​

{
  "error": {
    "code": "SLACK_API_ERROR",
    "message": "Channel not found",
    "details": {
      "slack_error": "channel_not_found",
      "channel": "C1234567890"
    }
  }
}

Security Considerations​

Token Management​

  • Secure token storage in environment variables
  • Token validation before API requests
  • No token persistence or logging
  • Automatic token refresh handling

Request Validation​

  • Input parameter validation using Pydantic models
  • Type checking and sanitization
  • Rate limiting awareness
  • Error message sanitization

Performance Optimization​

Async/Await Pattern​

@server.call_tool()
async def handle_slack_post_message(
    arguments: SlackPostMessageInput
) -> list[types.TextContent]:
    # Async Slack API call
    response = await slack_client.chat_postMessage(...)
    return [types.TextContent(type="text", text=str(response))]

Connection Pooling​

  • Reuse of HTTP connections to Slack API
  • Efficient resource management
  • Automatic connection cleanup

Response Caching​

  • Optional caching for read operations
  • Configurable cache TTL
  • Cache invalidation strategies

Extensibility​

Adding New Tools​

  1. Define input/output models in slack_mcp/mcp/model/
  2. Implement tool handler function
  3. Register with @server.call_tool() decorator
  4. Add comprehensive error handling
  5. Include usage documentation

Custom Transport Implementation​

  1. Implement transport-specific server setup
  2. Add transport option to CLI
  3. Update configuration handling
  4. Test with MCP clients

Tool Composition​

  • Tools can call other tools internally
  • Shared utility functions
  • Common error handling patterns
  • Consistent response formatting

Monitoring and Debugging​

Logging​

  • Structured logging with context
  • Request/response tracing
  • Error stack traces
  • Performance metrics

Health Checks​

  • Server liveness probes
  • Slack API connectivity checks
  • Token validation status
  • Transport mechanism health

Metrics​

  • Tool usage statistics
  • Response time tracking
  • Error rate monitoring
  • Client connection metrics

This architecture provides a robust, scalable, and maintainable foundation for MCP-based Slack integration while supporting multiple deployment patterns and use cases.