Vkteams Bot

VKTeams-Bot (unofficial)

[](https://crates.io/crates/vkteams-bot) [](https://codecov.io/github/bug-ops/vkteams-bot) [](https://crates.io/crates/vkteams-bot) [](https://docs.rs/vkteams-bot) [](https://github.com/bug-ops/vkteams-bot/actions/workflows/rust.yml) [](https://github.com/bug-ops/vkteams-bot/actions/workflows/github-code-scanning/codeql) [](LICENSE)

> The modern, high-performance VK Teams Bot API toolkit for Rust 🦀 > Complete ecosystem: Client library + CLI tools + MCP server + Storage infrastructure

✨ Why?

• 🚀 Fast: Rust performance with zero-cost abstractions
• 🛠️ Complete Toolkit: Library, CLI, MCP server, and storage in one ecosystem
• 🤖 AI-Ready: Native Model Context Protocol (MCP) support for LLM integration
• 💾 Smart Storage: PostgreSQL + Vector search for semantic message analysis
• ⚡ Developer-First: Intuitive CLI with auto-completion and colored output
• 🏢 Enterprise-Grade: Memory-safe, concurrent, production-ready
• 📦 Modular: Use only what you need - each component works independently

🚀 Quick Start

Install CLI (Fastest way to get started)

cargo install vkteams-bot-cli

# Set your credentials (or use config file)
export VKTEAMS_BOT_API_TOKEN="your_token_here"
export VKTEAMS_BOT_API_URL="your_api_url"

# Send your first message
vkteams-bot-cli send-text -u user123 -m "Hello from Rust! 🦀"

Use as Library

[dependencies]
vkteams-bot = "0.11"
tokio = { version = "1.0", features = ["full"] }

use vkteams_bot::Bot;

#[tokio::main]
async fn main() -> Result> {
    let bot = Bot::with_default_version("API_TOKEN", "API_URL");
    
    // Send a message
    bot.send_text("chat_id", "Hello, World! 🌍").await?;
    
    // Listen for events
    let events = bot.get_events().await?;
    println!("Received {} events", events.len());
    
    Ok(())
}

🔧 Complete Ecosystem

| Component | Description | Version | |-----------|-------------|---------| | 📚 Core Library | High-performance async VK Teams Bot API client | vkteams-bot v0.11 | | 🖥️ CLI Tool | Feature-complete command-line interface with storage | vkteams-bot-cli v0.7 | | 🤖 MCP Server | AI/LLM integration via Model Context Protocol | vkteams-bot-mcp v0.4 | | ⚙️ Macros | Development productivity macros | vkteams-bot-macros |

🆕 Storage & AI Features

💾 Storage Infrastructure

• PostgreSQL Integration: Full event and message history storage
• Vector Search: Semantic search using pgvector extension
• AI Embeddings: OpenAI and Ollama support for text embeddings
• Smart Search: Full-text and semantic similarity search

🤖 MCP Integration

• 30+ AI Tools: Messages, files, chats, storage operations
• Interactive Setup: Automatic chat ID elicitation - no manual configuration needed
• Context Management: Automatic conversation context retrieval
• CLI-as-Backend: Unified architecture for consistency

🐳 Docker Support

# Start all services
docker-compose up -d

# Start only essential services
docker-compose --profile relational-database up -d

# Add vector search
docker-compose --profile vector-search up -d

🎯 Use Cases

• 🏢 Enterprise Chat Automation: HR bots, IT support, business process automation
• 🤖 AI-Powered Assistants: LLM integration with Claude, ChatGPT via MCP
• ⚡ DevOps Integration: CI/CD notifications, monitoring alerts, deployment status
• 📊 Business Intelligence: Data reporting, analytics dashboards, scheduled reports
• 🔍 Knowledge Management: Semantic search across chat history
• 🔧 Internal Tools: Custom workflows, approval processes, team coordination

🚀 CLI Highlights

# Interactive event monitoring with filtering
vkteams-bot-cli get-events -l true | grep "ALARM"

# Batch file operations
find ./reports -name "*.pdf" | xargs -I {} vkteams-bot-cli send-file -u team_lead -p {}

# Semantic search in message history
vkteams-bot-cli storage search-semantic "deployment issues last week"

# Get conversation context for AI
vkteams-bot-cli storage get-context -c chat123 --limit 50

# Storage statistics
vkteams-bot-cli storage stats

🤖 AI Integration (MCP)

Integrate VK Teams bots directly with AI assistants:

// Claude Desktop config
{
  "mcpServers": {
    "vkteams-bot": {
      "command": "vkteams-bot-mcp",
      "env": {
        "VKTEAMS_BOT_API_TOKEN": "your_token",
        "VKTEAMS_BOT_API_URL": "your_api_url",
        "DATABASE_URL": "postgresql://localhost/vkteams"
      }
    }
  }
}

> Note: Chat ID is no longer required in configuration! The MCP server automatically prompts for it when first used.

Now Claude can:

• Auto-configure: Chat ID is requested interactively when first needed
• Send messages and manage files
• Search chat history semantically
• Get conversation context
• Execute complex workflows

⚙️ Configuration

Create .config/shared-config.toml:

[api]
token = "your_bot_token"
url = "https://api.vk.com"

[storage]
[storage.database]
url = "postgresql://localhost/vkteams"
auto_migrate = true

[storage.embedding]
provider = "ollama"  # or "openai"
model = "nomic-embed-text"
endpoint = "http://localhost:11434"

[mcp]
enable_storage_tools = true
enable_file_tools = true

🛠️ Development

# Clone and build
git clone https://github.com/bug-ops/vkteams-bot
cd vkteams-bot
cargo build --release

# Run tests with coverage
cargo llvm-cov nextest report

# Check documentation
cargo doc --open

# Run with Docker
docker-compose up -d

📖 Documentation

• 📚 API Docs: docs.rs/vkteams-bot
• 🎯 VK Teams Bot API: teams.vk.com/botapi
• 📝 Examples: GitHub Examples
• 🤖 MCP Protocol: Model Context Protocol

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: bug-ops
• Source: bug-ops/vkteams-bot
• License: MIT
• Homepage: https://docs.rs/vkteams-bot/latest

Install and usage instructions live in the source repository linked above.