CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Project Overview

This is a Slack MCP (Model Context Protocol) Client written in Go that serves as a bridge between Slack and multiple MCP servers. It allows LLM models to interact with various tools through a unified Slack interface.

Core Architecture

Main Components

  • Slack Bot Client: Handles Slack integration via Socket Mode

  • MCP Client Manager: Manages connections to multiple MCP servers (HTTP/SSE, stdio)

  • LLM Provider Registry: Supports OpenAI, Anthropic, Ollama via LangChain gateway

  • Tool Discovery System: Dynamically registers tools from all connected MCP servers

  • RAG System: Retrieval-Augmented Generation with JSON and OpenAI vector stores

  • Agent Mode: LangChain-powered conversational agents with tool orchestration

  • Monitoring: Prometheus metrics for tool usage and LLM token tracking

Key Packages

  • cmd/main.go: Application entry point and initialization

  • internal/config/: Configuration management with environment variable overrides

  • internal/slack/: Slack client implementation and message formatting

  • internal/mcp/: MCP client implementations (SSE, HTTP, stdio)

  • internal/llm/: LLM provider factories and LangChain integration

  • internal/rag/: RAG providers and tool implementations

  • internal/handlers/: Tool handlers and LLM-MCP bridge

  • internal/monitoring/: Prometheus metrics

Build and Development Commands

Essential Commands

# Build the application
make build

# Run the application
make run

# Run tests
make test

# Run linting and formatting
make lint

# Check all (format, lint, vet, test)
make check

# Clean build artifacts
make clean

Testing Commands

# Run all tests with verbose output
go test -v ./...

# Run tests with race detection
go test -race ./...

# Run tests with coverage
go test -coverprofile=coverage.out ./...

Docker Commands

# Build Docker image
make docker-build

# Run with Docker Compose
docker-compose up -d

Configuration

Environment Variables Required

  • SLACK_BOT_TOKEN: Bot token for Slack API

  • SLACK_APP_TOKEN: App-level token for Socket Mode

  • OPENAI_API_KEY: OpenAI API key (default provider)

  • LLM_PROVIDER: Provider selection (openai, anthropic, ollama)

Configuration Files

  • mcp-servers.json: MCP server definitions and tool configurations

  • .env: Environment variables (optional)

  • Config supports both legacy format and new mcpServers format

RAG Configuration

RAG can be enabled via LLM provider config with rag_enabled: true. Supports JSON-based storage and OpenAI vector stores.

Development Patterns

MCP Server Integration

  1. MCP servers are configured in mcp-servers.json with command/args or URL

  2. Clients support stdio, HTTP, and SSE transport modes

  3. Tool discovery happens at startup with allow/block lists

  4. Failed servers are logged but don't crash the application

LLM Provider Pattern

  1. Factory pattern in internal/llm/ for provider creation

  2. LangChain gateway provides unified interface

  3. Environment variables override config file settings

  4. Supports native tools vs system prompt-based tools

Error Handling

  1. Domain-specific errors in internal/common/errors/

  2. Graceful degradation when MCP servers fail

  3. Comprehensive logging with structured fields

  4. Circuit breaker pattern for failed connections

Testing Strategy

  1. Unit tests for core business logic

  2. Integration tests for MCP client functionality

  3. Mock interfaces for external dependencies

  4. Test coverage tracking in CI/CD

Agent Mode vs Standard Mode

Standard Mode (Default)

  • Single-prompt interactions with tool descriptions in system prompt

  • Direct JSON tool call parsing and execution

  • Predictable token usage and conversation flow

Agent Mode

  • Multi-turn conversational interactions via LangChain agents

  • Context-aware tool usage decisions

  • Better user context integration and reasoning capabilities

  • Enable with use_agent: true in config

Monitoring and Observability

Prometheus Metrics

  • Tool invocation counters with error rates

  • LLM token usage histograms by model and type

  • Metrics endpoint at :8080/metrics (configurable)

Logging

  • Structured logging with configurable levels

  • Component-specific loggers for MCP servers

  • Debug mode for detailed MCP communication

Common Development Tasks

Adding New MCP Server

  1. Add server config to mcp-servers.json

  2. Test connection with --debug flag

  3. Verify tool discovery in logs

  4. Add to allow/block lists if needed

Adding New LLM Provider

  1. Create factory in internal/llm/

  2. Implement LangChain integration

  3. Add environment variable handling in config.go

  4. Update provider constants

Debugging MCP Issues

  1. Enable --mcpdebug for MCP client logs

  2. Check server initialization timeouts

  3. Verify NPM packages for JavaScript servers

  4. Test stdio vs HTTP transport modes

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