Sutra
# Sutra
mcp-name: io.github.4rgon4ut/sutra
**The MCP Context Engineering Engine**
Sutra is a Model Context Protocol (MCP) server that transforms how LLMs handle reasoning, memory, and orchestration. It provides a "Standard Library" of cognitive tools (Thinking Models), memory structures (Cells), and multi-agent patterns (Organs).
## Installation
### Using `uv` (Recommended)
```bash
uv tool install context-engineering-mcp
```
### Using `pip`
```bash
pip install context-engineering-mcp
```
## Configuration
Select your agent below and copy-paste the config.
<details>
<summary>Claude Desktop</summary>
Add to `claude_desktop_config.json`:
```json
{
"mcpServers": {
"sutra": {
"command": "uv",
"args": ["tool", "run", "context-engineering-mcp"]
}
}
}
```
</details>
<details>
<summary>Claude Code</summary>
Run this in your terminal:
```bash
claude mcp add sutra uv tool run context-engineering-mcp
```
</details>
<details>
<summary>Aider</summary>
Run aider with the mcp flag:
```bash
aider --mcp "uv tool run context-engineering-mcp"
```
Or add to `.aider.conf.yml`:
```yaml
mcp: ["uv tool run context-engineering-mcp"]
```
</details>
<details>
<summary>Gemini CLI</summary>
Add to `~/.gemini/settings.json`:
```json
{
"mcpServers": {
"sutra": {
"command": "uv",
"args": ["tool", "run", "context-engineering-mcp"]
}
}
}
```
</details>
<details>
<summary>Cursor / Windsurf</summary>
In MCP settings, add a new server:
- **Name**: Sutra
- **Type**: command
- **Command**: `uv tool run context-engineering-mcp`
</details>
<details>
<summary>Codex</summary>
Add to your config (TOML):
```toml
[mcp_servers.sutra]
command = "uv"
args = ["tool", "run", "context-engineering-mcp"]
```
</details>
## Core Features (v0.1.0)
### 1. The Gateway (Router)
Sutra automatically analyzes your request to decide the best strategy:
- **YOLO Mode**: For immediate tasks ("Fix this bug"), it routes to specific cognitive tools.
- **Constructor Mode**: For system design ("Build a bot"), it routes to the Architect.
### 2. The Architect
Generates blueprints for custom agents, combining:
- **Thinking Models**: `understand_question`, `verify_logic`, `backtracking`, `symbolic_abstract`.
- **Memory Cells**: `key_value` (State), `windowed` (Short-term), `episodic` (Long-term).
- **Organs**: `debate_council` (Multi-perspective), `research_synthesis` (Deep Dive).
### 3. The Librarian
A manual discovery tool (`get_technique_guide`) that lets you or the agent browse the full catalog of Context Engineering techniques.
## Development
**Requirements**: Python 3.10+, `uv` (optional but recommended).
1. Clone the repo:
```bash
git clone https://github.com/4rgon4ut/sutra.git
cd sutra
```
2. Install dependencies:
```bash
uv sync --all-extras
# OR
pip install -e ".[dev]"
```
3. Run tests:
```bash
pytest
```
## License
MITTDQS
Scored across 12 tools
Each tool has a clearly distinct purpose: analyzing complexity, backtracking, architecture design, retrieving various templates (cell protocol, molecular, organ, prompt program, protocol shell, technique guide), symbolic abstraction, question decomposition, and logic verification. No overlapping functionality.
Most tools follow a verb_noun pattern (e.g., analyze_task_complexity, get_cell_protocol). However, 'backtracking' is a gerund and 'symbolic_abstract' combines an adjective with a verb, deviating slightly from the prevailing pattern.
With 12 tools, the server is well-scoped for its domain of context engineering. Each tool earns its place, covering analysis, design, template retrieval, and verification without being overwhelming or sparse.
The tool set covers core workflows: analyzing tasks, designing architecture, retrieving building blocks, and verifying logic. A minor gap is the lack of tools for executing or instantiating the designed blueprints, but the provided surface is largely complete for planning and template access.