Signals made human.
Tyto Sensory Labs exists to make dense technical output easier to feel, read, and trust.
The broader idea behind it is MCP4H™ - the Multimodal Communications Protocol For Humanity: a structured way of turning raw machine output into cues that are more coherent, more intuitive, and more usable for real people under real conditions.
The problem is straightforward. Modern systems generate enormous amounts of information, but much of it still reaches people in forms that are hard to parse quickly: dashboards, graphs, raw telemetry, logs, alerts, or fragmented signals spread across multiple interfaces. In many environments, that is not just inconvenient. It is a communication problem.
MCP4H is my attempt to address that problem through translation rather than accumulation. The aim is not simply to output more information, but to wrap data in a structured envelope that can drive different output types - haptics, audio, visuals, lighting, or combinations of them - depending on what the person on the receiving end actually needs.
The first practical proving ground for that idea is MCP4SH™, which applies this thinking to sim-racing haptics. It uses sim telemetry as raw input, then works to shape that output into cues that feel more readable, more layered, and less like undifferentiated noise. That makes sim racing a useful testbed: the feedback loop is fast, the signal density is high, and the value of clearer machine-to-human communication becomes obvious very quickly.
Today, MCP4H is in an early but real state: documented, structured, and actively being tested through working implementations and supporting schemas, examples, and harmonization concepts. It is not a finished universal standard yet, but it is no longer just an idea either.
MCP4H is also complementary to the wider Model Context Protocol (MCP) ecosystem, not a replacement for it. MCP provides a standard way for AI applications to connect to external systems and expose resources, tools, and prompts. MCP4H focuses on a different but related layer: once information is available, how should it be normalized, prioritized, and delivered so that a person can understand it quickly and act on it with confidence?
The long-term ambition is simple: less noise, better interpretation, and more trustworthy cues between machines and people.
The protocol layer
A grammar for awareness.
Machines produce too much raw output
most people cannot interpret it quickly
MCP4H™ (Multimodal Communications Protocol For Humanity) is the layer that helps turn that output into clearer, perhaps more intuitive cues
The idea behind it is simple: turn dense data into communication people can use, more naturally, intuitively.
Modern systems produce vast amounts of data. They the problem is us having to make that data easy for people to understand, especially under pressure, in high volumes at lightspeed. Too often, important information shows up as scattered alerts, raw numbers, graphs, or isolated signals that force people to do the translation work themselves.
This is designed to close that gap.
It provides a structured way to take any variety of data (be it system, software, hardware, cognitive, research,...) and wrap it so it can be projected across visual, audio, and haptic channels. Instead of leaving people to decode disconnected outputs, MCP4H aims to organize information into communication that feels more coherent, more intuitive, and easier to act on in real time.
The goal is better, more intuitively interpretable signals.
At its core, MCP4H focuses on things like context, priority, timing, and intent so that different technologies can communicate in ways that are easier for us to read, trust, and respond to. That could mean helping a driver feel what a car is doing more clearly, helping an operator notice the right warning sooner, or helping technical systems present information in ways that are less overwhelming and more useful than yet another dashboard.
In (relatively) simple terms, MCP4H is about making data feel less like data and more like understanding.
MCP4H also strengthens the human side of AI workflows built on MCP.
MCP gives AI systems a common way to access tools, data, and structured prompts. MCP4H adds a people-facing layer on top of that by helping shape outputs into communication that is easier to inspect, compare, and sanity-check. The aim is less black-box ambiguity, more clarity about what the system is surfacing and how it should be understood. That matters most in places where trust, timing, and accuracy are not optional.
The flagship implementation
From telemetry to weighted movement information.
MCP4SH® - String Theory Haptics for SimHub is a plugin built on the protocol's principles. It aims to make sim-racing haptics feel more coherent. More like a connected chassis, less like a pile of buzzers fighting for attention. v1.1 adds the Setup Assistant, so users can select the shakers on their rig, test-pulse channels, and generate a matching SimHub sound output profile without needing to understand every routing detail first. The codec still does the haptics work. The Setup Assistant helps people actually get it wired and mapped.
Licensing
Free gives you the core MCP4SH String Theory Haptics experience and the Setup Assistant. A license gives you the extra control layer for shaping the feel around your rig, and directly supports continued development. The current early-adopter Pioneer option is 12.99 for up to 2 machines. Store availability is the source of truth for what is actually live.
In plain English: free gets you working. A license gives you more control over how it feels on your rig.