In a latest version of The Sequence Engineering e-newsletter, “Why Did MCP Win?,” the authors level to context serialization and trade as a purpose—maybe crucial purpose—why everybody’s speaking concerning the Mannequin Context Protocol. I used to be puzzled by this—I’ve learn quite a lot of technical and semitechnical posts about MCP and haven’t seen context serialization talked about. There are tutorials, lists of accessible MCP servers, and way 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 trade” 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 purposes via a normal 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 offers methods for purposes to inform fashions about their capabilities in order that brokers can use these capabilities to finish a process. However it additionally provides fashions the means to share the present context with different purposes that may make use of it. For conventional purposes like GitHub, sharing context is meaningless. For the most recent technology of purposes that use networks of fashions, sharing context opens up new potentialities.
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Right here’s a comparatively easy instance. It’s possible you’ll be utilizing AI to write down a program. You add a brand new characteristic, check it, and it really works. What occurs subsequent? From inside your IDE, you may name conventional purposes like Git to commit the adjustments—not a giant deal, and a few AI instruments like Aider can already do this. However you additionally wish to ship a message to your supervisor and staff members describing the undertaking’s present state. Your AI-enhanced IDE may be capable of 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 bundle every thing related about your context and ship it to Gemini, with directions to resolve what’s vital, generate the message, and ship the message by way of Gmail after it has been created. That’s completely different: As an alternative of an AI utilizing a conventional software, now we’ve got two AIs collaborating to finish a process. There may even be a dialog between the AIs about what to say within the message. (And it’s good to verify that the consequence meets your expectations—vibe emailing a boss looks like 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 complicated. Think about an AI software that helps farmers plan what they’ll plant. That software may 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 applying would most likely 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 job of predicting climate, and the monetary business is utilizing AI to do financial modeling. One might think about doing this all on an enormous “know every thing” LLM (perhaps GPT-6 or 7). However one factor we’re studying is that smaller specialised fashions usually outperform massive generalist fashions of their areas of specialization. An AI that fashions crop costs ought to have entry to quite a lot of vital information that isn’t public. So ought to fashions that forecast seed costs, fertilizer costs, and gas costs. All of those fashions are most likely 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 data from these specialised fashions by sending context to them: what the farmer desires to know, after all, but in addition the placement of the fields, climate patterns over the previous yr, the farm’s manufacturing over the previous few years, the farm’s technological capabilities, the supply of sources 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 consequence; a dialog must occur between the specialist AIs as a result of every reply will affect the others. It could be potential to foretell the climate with out understanding about economics, however you may’t do agricultural economics in case 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 pupil can construct an app that sends a immediate to ChatGPT and screen-scrapes the outcomes. Anthropic’s pc 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 yr. We are able to construct networks of AI fashions and brokers. That’s what MCP helps. We couldn’t think about this software only a few 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 provides us the instruments to construct synthetic social intelligence.
The business has been speaking about brokers for a while now—dozens of years, actually. The newest burst of agentic dialogue began simply over a yr in the past. For the previous yr we’ve had fashions that had been ok, however we had been lacking an vital piece of the puzzle: the flexibility to ship context from one mannequin to a different. MCP offers a few of the lacking items. Google’s new A2A protocol offers 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’s going to we construct?
