ASCII Banner MCP Server
An MCP server for getting ASCII banners
README
ASCII Banner MCP Server
A classical Model Context Protocol (MCP) server that generates ASCII art banners from any string using pyfiglet.
<a href="https://glama.ai/mcp/servers/@guilyx/ascii-banner-mcp"> <img width="380" height="200" src="https://glama.ai/mcp/servers/@guilyx/ascii-banner-mcp/badge" /> </a>
Features
get_fonts— List all available pyfiglet font names (viaFigletFont.getFonts()).generate_banner— Render a string as ASCII art with a chosen font (viafiglet_format(text, font=...)).
Requirements
- Python ≥ 3.10
mcp,pyfiglet
Installation
From the project root:
pip install -e .
For development (tests):
pip install -e ".[dev]"
Usage
Run the server (stdio)
MCP clients typically run the server as a subprocess and talk over stdio:
python -m ascii_banner_mcp.server
Or after install:
ascii-banner-mcp
Config example
Copy and adjust one of the examples in config/:
- stdio (local):
config/mcp-config.example.json—command+argsfor Cursor, Claude Desktop, etc. - streamable-http (e.g. Docker):
config/mcp-config-streamable-http.example.json—url: "http://localhost:8000/mcp"when the server runs over HTTP.
Configure your MCP client
Add the server to your MCP client config (e.g. Cursor, Claude Desktop). Example (stdio):
{
"mcpServers": {
"ascii-banner": {
"command": "python",
"args": ["-m", "ascii_banner_mcp.server"]
}
}
}
If you use a virtualenv, use the full path to that Python:
{
"mcpServers": {
"ascii-banner": {
"command": "/path/to/venv/bin/python",
"args": ["-m", "ascii_banner_mcp.server"]
}
}
}
Tools
| Tool | Description |
|---|---|
get_fonts |
Returns a list of available font names. Use this to discover fonts for generate_banner. |
generate_banner |
Renders text as ASCII art. Parameters: text (required), font (optional, default "standard"). Use fonts from get_fonts() (e.g. "slant", "block", "big"). |
Example (equivalent to your snippet):
from pyfiglet import figlet_format
print(figlet_format("Hello", font="slant"))
Via this MCP server: call generate_banner with text="Hello" and font="slant".
MCP Inspector
Use MCP Inspector to test and debug the server.
Option 1 — stdio (local process)
- Run the Inspector:
npx @modelcontextprotocol/inspector - In the UI, add a server with Stdio transport.
- Set Command to
python(or full path to your Python/venv). - Set Args to
-m,ascii_banner_mcp.server. - Ensure the project is installed (
pip install -e .) or set cwd to the project root and usepython -m ascii_banner_mcp.server.
Option 2 — streamable-http (Docker or local)
- Start the server over HTTP:
- Docker:
docker compose -f .docker/docker-compose.yml up --build(see Docker below). - Local:
MCP_TRANSPORT=streamable-http python -m ascii_banner_mcp.server(serves athttp://127.0.0.1:8000/mcp).
- Docker:
- Run the Inspector:
npx @modelcontextprotocol/inspector - Add a server with Streamable HTTP (or URL) and set the URL to
http://localhost:8000/mcp.
Custom ports (Inspector):
CLIENT_PORT=8080 SERVER_PORT=9000 npx @modelcontextprotocol/inspector
Docker
Run the MCP server in a container. Use the .docker/ setup:
stdio (default) — client runs the container and talks via stdin/stdout:
docker build -f .docker/Dockerfile -t ascii-banner-mcp .
docker run -i --rm ascii-banner-mcp
streamable-http (for Inspector or URL-based clients):
docker compose -f .docker/docker-compose.yml up --build
Server is at http://localhost:8000/mcp. Use config/mcp-config-streamable-http.example.json or point MCP Inspector at that URL.
Development
- Tests:
pytest - Lint:
ruff check src tests - Format:
black src tests - Pre-commit: Black, Ruff, and conventional-commit message checks (e.g.
feat:,fix:). Install:pip install -e ".[dev]"thenpre-commit installandpre-commit install --hook-type commit-msg. Run manually:pre-commit run --all-files.
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.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
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.