In a current version of The Sequence Engineering e-newsletter, “Why Did MCP Win?,” the authors level to context serialization and alternate as a motive—maybe a very powerful motive—why everybody’s speaking in regards to the Mannequin Context Protocol. I used to be puzzled by this—I’ve learn loads of technical and semitechnical posts about MCP and haven’t seen context serialization talked about. There are tutorials, lists of obtainable MCP servers, and rather more however nothing that mentions context serialization itself. I used to be much more puzzled after studying via the MCP specification, wherein the phrases “context serialization” and “context alternate” don’t seem.
What’s happening? The authors of the Sequence Engineering piece discovered the larger image, one thing extra substantial than simply utilizing MCP to let Claude management Ableton. (Although that’s enjoyable. Suno, beware!) It’s not nearly letting language fashions drive conventional functions via an ordinary API. There isn’t a separate part on context serialization as a result of all of MCP is about context serialization. That’s why it’s referred to as the Mannequin Context Protocol. Sure, it gives methods for functions to inform fashions about their capabilities in order that brokers can use these capabilities to finish a activity. However it additionally offers fashions the means to share the present context with different functions that may make use of it. For conventional functions like GitHub, sharing context is meaningless. For the most recent era of functions that use networks of fashions, sharing context opens up new potentialities.
Right here’s a comparatively easy instance. Chances are you’ll be utilizing AI to put in writing a program. You add a brand new characteristic, take a look at it, and it really works. What occurs subsequent? From inside your IDE, you may name conventional functions like Git to commit the adjustments—not an enormous deal, and a few AI instruments like Aider can already try this. However you additionally wish to ship a message to your supervisor and crew members describing the venture’s present state. Your AI-enhanced IDE would possibly be capable to generate an electronic mail. However Gmail has its personal integrations with Gemini for writing electronic mail, and also you’d favor to make use of that. So your IDE can package deal every little thing related about your context and ship it to Gemini, with directions to resolve what’s vital, generate the message, and ship the message through Gmail after it has been created. That’s completely different: As a substitute of an AI utilizing a conventional software, now we’ve two AIs collaborating to finish a activity. There may even be a dialog between the AIs about what to say within the message. (And you have to affirm that the outcome meets your expectations—vibe emailing a boss looks as if an antipattern.)
Now we are able to begin speaking about networks of AIs working collectively. Right here’s an instance that’s solely considerably extra advanced. Think about an AI software that helps farmers plan what they’ll plant. That software would possibly wish to use:
- An economics service to forecast crop costs
- A service to forecast seed costs
- A service to forecast fertilizer costs
- A service to forecast gas costs
- A climate service
- An agronomy mannequin that predicts what crops will develop effectively on the farm’s location
The appliance would in all probability require a number of extra providers that I can’t think about—is there an entomology mannequin that may forecast insect infestations? (Sure, there may be.) AI can already do a very good job of predicting climate, and the monetary trade is utilizing AI to do financial modeling. One may think about doing this all on a large “know every little thing” LLM (perhaps GPT-6 or 7). However one factor we’re studying is that smaller specialised fashions typically outperform massive generalist fashions of their areas of specialization. An AI that fashions crop costs ought to have entry to loads of vital knowledge that isn’t public. So ought to fashions that forecast seed costs, fertilizer costs, and gas costs. All of those fashions are in all probability subscription-based providers. It’s doubtless that a big farming enterprise or cooperative would develop proprietary in-house fashions.
The farmer’s AI wants to collect info from these specialised fashions by sending context to them: what the farmer desires to know, in fact, but in addition the placement of the fields, climate patterns over the previous 12 months, the farm’s manufacturing over the previous few years, the farm’s technological capabilities, the supply of assets like water, and extra. Moreover, it’s not only a matter of asking every of those fashions a query, getting the solutions, and producing a outcome; a dialog must occur between the specialist AIs as a result of every reply will affect the others. It might be doable to foretell the climate with out realizing about economics, however you may’t do agricultural economics when you don’t perceive the climate. That is the place MCP’s worth actually lies. Constructing an software that asks fashions questions? That’s positively helpful, however any highschool scholar can construct an app that sends a immediate to ChatGPT and screen-scrapes the outcomes. Anthropic’s laptop use API goes a step additional by automating the click and screen-scraping. The true worth is in connecting fashions to one another to allow them to have conversations—so {that a} mannequin that predicts the worth of corn can uncover climate forecasts for the approaching 12 months. We will construct networks of AI fashions and brokers. That’s what MCP helps. We couldn’t think about this software just some years in the past. Now we are able to’t simply think about it; we are able to begin constructing it. As Blaise Agüera y Arcas argues, intelligence is collective and social. MCP offers us the instruments to construct synthetic social intelligence.
The trade has been speaking about brokers for a while now—dozens of years, actually. The newest burst of agentic dialogue began simply over a 12 months in the past. For the previous 12 months we’ve had fashions that had been ok, however we had been lacking an vital piece of the puzzle: the power to ship context from one mannequin to a different. MCP gives a few of the lacking items. Google’s new A2A protocol gives extra of them. That’s what context serialization is all about, and that’s what it allows: networks of collaborating AIs, every performing as a specialist. Now, the one query is: What is going to we construct?
