CEDRUS MCP server

CEDRUS

<!-- mcp-name: io.github.cedrus-mcp/cedrus-mcp -->

Reasoning scaffolding that supercharges your small AI.

CEDRUS gives a local model an argument map to think in: the model lays out claims and the reasons for and against them, and the map keeps everything in place.

model doesn't have to hold it in its head, and a small model can work through a debate that would otherwise swamp it.

• A scaffold, not a scratchpad. The map records which reason answers which claim, so the

server. A move that doesn't fit is refused with an explanation, so mistakes get fixed instead of piling up.

• Structure is checked for the model. IDs, sides and placement are worked out by the

"add an objection to A3", "what supports A7?".

• You see the reasoning. Every step lands in a map you can read, question and extend:

plain text and JSON.

• Local and private. Made for small models on your own machine, with maps saved as

What a map looks like

- C1 Legalisation of Soft Drugs
  - A1 Minimize destructive activity [con] — attacks C1
  - A2 No harm, no ban [pro] — supports C1
    - A3 Law must protect [con] — attacks A2
      - A4 Soft drugs are harmful [con] — supports A3 (also attacks A2, supports A1)
  - A5 Lifestyle decision [pro] — supports C1
    - A6 Moral leadership [con] — attacks A5
  - A7 Alcohol and tobacco analogy [pro] — supports C1
    - A8 Tobacco and alcohol more dangerous [pro] — supports A7
    - A9 Major differences [con] — attacks A7
    - A10 Poor reason [con] — undercuts A7
  - A11 Listen to society [pro] — supports C1
  - A12 Argument from addiction [con] — attacks C1
    - A13 Soft drugs are addictive [con] — supports A12
    - A14 Addiction no reason to ban [pro] — undercuts A12
  - A15 Slippery slope [con] — attacks C1
    - A16 Coffee houses [pro] — attacks A15
  - A19 Tax revenue [pro] — supports C1

This is the outline. The full view the model reads also has every claim and argument in full, and notes which ones nothing has challenged yet.

Quick start

1. Install uv

CEDRUS is started by uv, which downloads and runs it for you.

# macOS and Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

2. Add CEDRUS to your app

Each setup below saves your maps to ~/cedrus-maps, a folder in your home directory. Use any folder you like, or leave out --save-dir and its path if you don't want files.

Whichever app you use, the model has to support tool calling.

LM Studio

In the right sidebar, open the Program tab and choose Install → Edit mcp.json. Add CEDRUS under mcpServers:

{
  "mcpServers": {
    "cedrus": {
      "command": "uvx",
      "args": ["cedrus-mcp", "--save-dir", "~/cedrus-maps"]
    }
  }
}

Jan

Go to Settings → MCP Servers, click +, and fill in:

For a local model, turn on tool calling in the model's settings (the edit button under Model Capabilities).

Goose

Run goose configure, choose Add Extension → Command-line Extension, name it cedrus, and give the command uvx cedrus-mcp --save-dir ~/cedrus-maps. In Goose Desktop, the same is under Extensions → Add custom extension.

Or add it to ~/.config/goose/config.yaml (%APPDATA%\Block\goose\config\config.yaml on Windows) yourself:

extensions:
  cedrus:
    name: cedrus
    type: stdio
    cmd: uvx
    args: [cedrus-mcp, --save-dir, ~/cedrus-maps]
    enabled: true
    timeout: 300

Goose can run local models through Ollama, so this is also the way to use CEDRUS with Ollama.

Other MCP clients

Any client that can start a local (stdio) MCP server works: the command is uvx, the arguments are cedrus-mcp and any options below.

Try it

Shortened. The full README is on GitHub.