Toot

Train of Operadic Thought (TOOT) is an MCP server that captures context, reasoning chains, and positive work patterns to create a reinforced, reusable knowledge loop across conversations. It enables you to reference past successes when starting new tasks and automatically records effective approaches for future use.

How to use

You operate TOOT through an MCP client that interacts with the TOOT MCP server to set intentions, perform work, and capture successful patterns. Start by defining your goal with a clear intention, then execute tasks while referencing past successes. When you or the system completes a successful action, trigger a positive feedback moment to have TOOT record the pattern for future reuse. Use the standard workflow: set intention, do the work, capture success, and let TOOT grow your compound reasoning over time.

Key capabilities you will leverage include creating and updating train-of-thought reasoning chains, recording successful outcomes, and automatically integrating feedback hooks that help the assistant learn what good performance looks like. You can use these tools to build robust context for ongoing projects and ensure that patterns that lead to success are preserved and reused.

How to install

Prerequisites: Python 3.8+ and pip must be installed on your system.

Install the TOOT MCP package with the following command:

pip install toot-mcp

Configure the MCP server in your client setup to run TOOT as a local process. Use the provided MCP configuration snippet to register the server.

Configuration and deployment

The MCP configuration defines TOOT as a local (stdio) server. This runs as a separate process that the client can start and stop as part of its integration workflow.

{
  "mcpServers": {
    "toot": {
      "command": "python",
      "args": ["-m", "toot_mcp"],
      "env": {}
    }
  }
}

Operational notes

Environment variables for TOOT, when needed, should be added to the server configuration and documented in your deployment notes. TOOT stores its data in temporary storage as JSON files with timestamps and reasoning chains, so monitor disk usage if you enable frequent captures.

Examples and notes

Use the following patterns to capture and reference success: you can call the function to record a successful pattern, and you can set an intention to guide future work.

Tools and capabilities

TOOT provides a set of core functions to manage and reuse success patterns and reasoning. The primary tools are explained below to help you implement and use them effectively.

Security and data handling

Manage access to the TOOT MCP server with appropriate permissions. Sensitive success patterns and reasoning data should be stored in a secure location and governed by your organization’s data retention policies.

Troubleshooting

If the TOOT server fails to start, verify Python is installed, the correct module is available, and that the environment can access any required resources. Check logs produced by the stdio process for startup errors and ensure the working directory contains the module toot_mcp.

Notes

TOOT is designed to work within a broader Compound Intelligence ecosystem, enabling validated conceptual reasoning and continuous improvement across conversations.

Available tools

user_said_i_did_a_good_job

Records successful patterns for reinforcement learning with detailed context such as name, domain, process, and description.

i_need_to_do_a_good_job

Sets intention for excellent work and references past successes by domain to guide future actions.

create_train_of_thought

Creates a new reasoning chain for complex problem solving, providing a structured path for later updates.

update_train_of_thought

Appends to an existing reasoning chain to preserve the integrity of the progression.