MCP Servers
What is MCP?
The Model Context Protocol (MCP) is an open standard that lets AI models interact with external tools and data sources. AQBot acts as an MCP client — you add MCP servers, and the AI can call the tools they expose during a conversation.
Transport Protocols
AQBot supports three transport protocols for communicating with MCP servers:
| Protocol | Connection | Use Case | Configuration |
|---|---|---|---|
| Stdio | Local process | Tools installed on your machine, launched via npx, uvx, python, etc. | command + args + optional env |
| SSE | Remote server | Server-Sent Events endpoint hosted on a remote machine or cloud service | url |
| StreamableHTTP | Remote server | HTTP streaming endpoint, the newer alternative to SSE | url |
Adding MCP Servers
Form Creation
- Go to Settings → MCP Servers.
- Click Add MCP Server.
- Enter a name and select the transport protocol.
- Fill in the fields for your chosen protocol:
- Stdio — command to run, arguments (JSON array), environment variables (JSON object), timeout.
- SSE — endpoint URL, timeout.
- StreamableHTTP — endpoint URL, timeout.
- Click Save.
JSON Import
Click JSON Import and paste a configuration object. AQBot accepts the standard MCP JSON format:
Stdio server
{
"mcpServers": {
"fetch": {
"command": "uvx",
"args": ["mcp-server-fetch"]
}
}
}SSE server
{
"mcpServers": {
"remote-tools": {
"type": "sse",
"url": "https://example.com/sse"
}
}
}StreamableHTTP server
{
"mcpServers": {
"remote-http": {
"type": "streamablehttp",
"url": "https://example.com/mcp"
}
}
}Multiple servers at once
{
"mcpServers": {
"fetch": {
"command": "uvx",
"args": ["mcp-server-fetch"]
},
"filesystem": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-filesystem", "/Users/me/projects"]
},
"remote": {
"type": "sse",
"url": "https://example.com/sse"
}
}
}Configuration Examples
Web Fetching — mcp-server-fetch
Fetches web pages and converts them to readable text for the AI.
{
"mcpServers": {
"fetch": {
"command": "uvx",
"args": ["mcp-server-fetch"]
}
}
}File Operations — @modelcontextprotocol/server-filesystem
Gives the AI read/write access to specific directories on your machine.
{
"mcpServers": {
"filesystem": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-filesystem",
"/Users/me/documents",
"/Users/me/projects"
]
}
}
}Remote SSE Server
Connect to a remote tool server over Server-Sent Events.
{
"mcpServers": {
"cloud-tools": {
"type": "sse",
"url": "https://mcp.example.com/sse"
}
}
}Stdio with Environment Variables
Pass API keys or configuration to the server process via environment variables.
{
"mcpServers": {
"weather": {
"command": "python",
"args": ["-m", "weather_server"],
"env": {
"WEATHER_API_KEY": "your-api-key"
}
}
}
}Permission Policies
Each MCP server can be assigned a permission policy that controls how tool calls are authorized.
| Policy | Behavior |
|---|---|
| Ask Every Time | A confirmation dialog appears before every tool call. Safest option. |
| Allow Safe Operations | Read-only operations execute automatically; write or destructive operations require confirmation. |
| Allow All | All tool calls execute without confirmation. Use only with servers you fully trust. |
WARNING
The Allow All policy lets the AI execute any tool — including file writes and shell commands — without asking. Use it only with trusted, well-scoped servers.
Built-in Tools
AQBot ships with built-in tools that are available without adding any external MCP server:
| Tool | Description |
|---|---|
| @aqbot/fetch | Fetch web pages and HTTP resources |
| @aqbot/search-file | Search for files on your local file system |
Built-in tools are listed in the MCP Servers settings alongside your custom servers and can be enabled or disabled individually.
Tool Execution Panel
When the AI calls an MCP tool during a conversation, AQBot displays a tool execution panel inline in the chat. The panel shows:
- The tool name and the server it belongs to.
- The input arguments sent to the tool.
- The output returned by the tool.
- Execution status (running, succeeded, or failed).
Click on any tool call in the conversation to expand its details. This makes it easy to verify what the AI did and debug unexpected results.
Next Steps
- API Gateway — expose your providers as a local API server
- Getting Started — return to the quick start guide
