textual-mcp-server
An MCP server that lets AI agents launch, interact with, and inspect Textual TUI applications headlessly.
README
textual-mcp-server
An MCP server that lets AI agents launch, interact with, and inspect Textual TUI applications headlessly. Drive any Textual app through its full lifecycle — click buttons, type text, read widget state, take screenshots — all via the Model Context Protocol.
Features
- Headless app execution — Launch any Textual app without a terminal, powered by Textual's built-in
Pilottesting API - Full interaction toolkit — Click, type, press keys, and hover over widgets using CSS selectors
- Rich state inspection — Snapshot the widget tree, query widgets by selector, and extract type-specific properties from 16+ widget types
- Multi-session support — Run multiple apps concurrently with isolated sessions
- Error tracking — Automatic collection of worker errors and app exceptions via message hooks
- Screenshot capture — Export the current screen as plain text or SVG
Installation
Requires Python 3.10+.
pip install textual-mcp-server
For development:
git clone https://github.com/discohead/textual-mcp-server.git
cd textual-mcp-server
pip install -e ".[dev]"
Quick Start
As a standalone server
textual-mcp
With Claude Code
claude mcp add textual -- textual-mcp
Or add manually to your MCP configuration (e.g., ~/.claude.json or project .mcp.json):
{
"mcpServers": {
"textual": {
"command": "textual-mcp"
}
}
}
Typical workflow
1. textual_launch("my_app.py") → session_id
2. textual_snapshot(session_id) → widget tree + focus + bindings
3. textual_click(session_id, "#submit") → interact
4. textual_screenshot(session_id) → visual output
5. textual_stop(session_id) → cleanup
Tools
Lifecycle
| Tool | Description |
|---|---|
textual_launch |
Launch a Textual app headlessly. Accepts a file path (app.py), path with class (app.py:MyApp), or module path (mypackage.module:MyApp). Returns a session_id. |
textual_stop |
Stop a running session and return any collected errors. |
Interaction
| Tool | Description |
|---|---|
textual_press |
Simulate key presses (e.g., ["enter"], ["ctrl+s"]). |
textual_click |
Click a widget by CSS selector with optional offset and repeat count. |
textual_type_text |
Type text into the focused input widget, with optional submit (Enter). |
textual_hover |
Hover the mouse over a widget by CSS selector. |
Observation
| Tool | Description |
|---|---|
textual_snapshot |
Snapshot the widget tree with ref markers, focus state, active key bindings, and errors. |
textual_screenshot |
Capture the current screen as plain text or SVG. |
textual_query |
Query widgets matching a CSS selector. Returns type, ID, classes, and extracted properties. |
textual_get_screen_stack |
Get the current screen stack with modal indicators. |
Assertion & Waiting
| Tool | Description |
|---|---|
textual_wait_for |
Wait for a condition: idle, animation, workers (all complete), or widget (selector appears). |
textual_check_errors |
Check for collected worker errors and app exceptions. |
Architecture
textual_mcp/
├── server.py # FastMCP server, tool registration, and tool implementations
├── session.py # AppSession — headless app lifecycle via Pilot
├── session_manager.py # Multi-session management
├── app_loader.py # Dynamic app loading from file or module path
├── error_collector.py # Message hook for worker error aggregation
└── serializers/
├── widget_tree.py # DOM → indented text tree with [ref=N] markers
└── widget_state.py # Type-specific property extraction (16 widget types)
Key design decisions:
AppSessionwraps Textual'sApp.run_test()to provide launch/stop semantics with a persistentPilothandleWidgetTreeSerializerproduces LLM-friendly text output — interactive widgets get[ref=N]markers; scrollbars and hidden widgets are excludedWidgetStateExtractoruses an ordered isinstance registry to extract properties from Input, Button, DataTable, TextArea, Tree, and 11 other widget typesErrorCollectorhooks into Textual's message system to captureWorker.StateChangederrors without disrupting normal operation
Supported Widget Types
The state extractor provides rich property data for:
Input, Button, Static, Label, Checkbox, Switch, Select, TextArea, DataTable, Tree, ListView, OptionList, TabbedContent, ProgressBar, RadioSet, ContentSwitcher
Development
# Run tests
pytest
# Run a specific test
pytest tests/test_integration_calculator.py -v
Requirements
License
MIT
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
Neon Database
MCP server for interacting with Neon Management API and databases
E2B
Using MCP to run code via e2b.
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.