What is Agentassistant

agentassistant is an assistant designed for agents operating through the MCP framework, providing support and enhancing their efficiency.

Use cases

Use cases for agentassistant include automating routine inquiries, managing customer interactions, and providing agents with quick access to information and resources.

How to use

To use agentassistant, agents can integrate it into their MCP environment, following the setup instructions provided in the documentation to ensure seamless functionality.

Key features

Key features of agentassistant include real-time assistance, task automation, and data management capabilities tailored for agent workflows.

Where to use

agentassistant can be utilized in various sectors such as customer service, sales, and technical support, where agents require efficient tools to enhance their performance.

Agent Assistant

Agent Assistant is a system that allows AI agents to interact with human users through a web interface. It consists of three main components:

1. agentassistant-srv: The main server that handles RPC requests and serves the web interface
2. agentassistant-mcp: An MCP (Model Context Protocol) server that provides tools for AI agents
3. Web Interface: A modern React-based web application for user interaction

Architecture

AI Agent (Claude, etc.)
    ↓ (MCP Protocol)
agentassistant-mcp
    ↓ (Connect-Go RPC)
agentassistant-srv
    ↓ (WebSocket)
Web Interface (User)

Features

• MCP Integration: Provides ask_question and task_finish tools for AI agents
• Real-time Communication: WebSocket-based communication between server and web interface
• Modern Web UI: React-based interface with Shadcn/ui components
• Token-based Authentication: Simple token-based authentication system
• Cross-platform: Works on Linux, macOS, and Windows

Quick Start

1. Build the Components

# Build the server
go build -o agentassistant-srv ./cmd/agentassistant-srv

# Build the MCP server
go build -o agentassistant-mcp ./cmd/agentassistant-mcp

2. Start the Server

./agentassistant-srv

The server will start on port 8080 and serve:

• Web interface at http://localhost:8080
• WebSocket endpoint at ws://localhost:8080/ws
• RPC endpoints for Connect-Go

3. Configure and Start MCP Server

Edit agentassistant-mcp.toml:

agentassistant_server_host = "127.0.0.1"
agentassistant_server_port = 8080
agentassistant_server_token = "test-token"

Start the MCP server:

./agentassistant-mcp

4. Access the Web Interface

Open your browser and go to:

http://localhost:8080?token=test-token

Development

Building the Web Interface

The web interface is a React application located in the web/ directory.

cd web
npm install
npm run build

The built files will be placed in web/dist/ and automatically served by the server.

For development:

cd web
npm run dev

Running Tests

go test ./...

Configuration

MCP Server Configuration

Create agentassistant-mcp.toml:

agentassistant_server_host = "127.0.0.1"
agentassistant_server_port = 8080
agentassistant_server_token = "your-token-here"

Command Line Options

MCP Server:

./agentassistant-mcp -host localhost -port 8080 -token your-token -web

• -host: Server host (default: 127.0.0.1)
• -port: Server port (default: 8080)
• -token: Authentication token (default: test-token)
• -web: Open web interface in browser

API Reference

MCP Tools

ask_question

Ask a question to the user through the web interface.

Parameters:

• project_directory (string): Current project directory
• question (string): Question to ask the user
• timeout (number): Timeout in seconds (default: 600)

task_finish

Notify the user that a task has been completed.

Parameters:

• project_directory (string): Current project directory
• summary (string): Summary of the completed task
• timeout (number): Timeout in seconds (default: 600)

RPC Services

SrvAgentAssist

• AskQuestion(AskQuestionRequest) returns (AskQuestionResponse)
• TaskFinish(TaskFinishRequest) returns (TaskFinishResponse)

MCP Agent Assistant Interaction Rules

1. During any process, task, or dialogue, whether it’s for inquiries, responses, or completing phased tasks, the corresponding MCP tools must be called:
   • Use the ask_question tool when user feedback is needed.
   • Use the task_finish tool when a task is completed.
2. Whenever user feedback is received, if the feedback content is not empty, behavior must be adjusted according to the feedback, and the corresponding tools should be called again if necessary.
3. Only when the user explicitly states “end” or “no further interaction needed” can the calling of MCP tools be stopped, and the process is considered complete.
4. Unless an end instruction is received, all key steps must be confirmed with the user through MCP tools.
5. Before completing a task, the task_finish tool must be used to report the task completion status to the user and request confirmation.
6. When calling tools, ensure the correct project_directory parameter is provided to maintain contextual accuracy.

License

This project is licensed under the MIT License.