The Mannequin Context Protocol (MCP) is genuinely helpful. It offers individuals who develop AI instruments a standardized option to name capabilities and entry knowledge from exterior programs. As a substitute of constructing customized integrations for every knowledge supply, you’ll be able to expose databases, APIs, and inside instruments by way of a standard protocol that any AI can perceive.
Nonetheless, I’ve been watching groups undertake MCP over the previous 12 months, and I’m seeing a disturbing sample. Builders are utilizing MCP to rapidly join their AI assistants to each knowledge supply they’ll discover—buyer databases, help tickets, inside APIs, doc shops—and dumping all of it into the AI’s context. And since the AI is sensible sufficient to kind by way of a large blob of information and pick the components which can be related, all of it simply works! Which, counterintuitively, is definitely an issue. The AI cheerfully processes huge quantities of information and produces affordable solutions, so no person even thinks to query the strategy.
That is knowledge hoarding. And like bodily hoarders who can’t throw something away till their properties turn into so cluttered they’re unliveable, knowledge hoarding has the potential to trigger critical issues for our groups. Builders study they’ll fetch much more knowledge than the AI wants and supply it with little planning or construction, and the AI is sensible sufficient to take care of it and nonetheless give good outcomes.
When connecting a brand new knowledge supply takes hours as a substitute of days, many builders don’t take the time to ask what knowledge really belongs within the context. That’s how you find yourself with programs which can be costly to run and not possible to debug, whereas a complete cohort of builders misses the possibility to study the vital knowledge structure expertise they should construct strong and maintainable functions.
How Groups Study to Hoard
Anthropic launched MCP in late 2024 to provide builders a common option to join AI assistants to their knowledge. As a substitute of sustaining separate code for connectors to let AI entry knowledge from, say, S3, OneDrive, Jira, ServiceNow, and your inside DBs and APIs, you employ the identical easy protocol to offer the AI with all kinds of information to incorporate in its context. It rapidly gained traction. Firms like Block and Apollo adopted it, and groups in every single place began utilizing it. The promise is actual; in lots of instances, the work of connecting knowledge sources to AI brokers that used to take weeks can now take minutes. However that pace can come at a price.
Let’s begin with an instance: a small group engaged on an AI instrument that reads buyer help tickets, categorizes them by urgency, suggests responses, and routes them to the best division. They wanted to get one thing working rapidly however confronted a problem: They’d buyer knowledge unfold throughout a number of programs. After spending a morning arguing about what knowledge to drag, which fields have been mandatory, and the best way to construction the mixing, one developer determined to simply construct it, making a single getCustomerData(customerId) MCP instrument that pulls all the pieces they’d mentioned—40 fields from three totally different programs—into one large response object. To the group’s aid, it labored! The AI fortunately consumed all 40 fields and began answering questions, and no extra discussions or selections have been wanted. The AI dealt with all the brand new knowledge simply high-quality, and everybody felt just like the mission was heading in the right direction.
Day two, somebody added order historical past so the assistant may clarify refunds. Quickly the instrument pulled Zendesk standing, CRM standing, eligibility flags that contradicted one another, three totally different title fields, 4 timestamps for “final seen,” plus complete dialog threads, and mixed all of them into an ever-growing knowledge object.
The assistant stored producing reasonable-looking solutions, at the same time as the info it ingested stored rising in scale. Nonetheless, the mannequin now needed to wade by way of 1000’s of irrelevant tokens earlier than answering easy questions like “Is that this buyer eligible for a refund?” The group ended up with a knowledge structure that buried the sign in noise. That further load put stress on the AI to dig out that sign, resulting in critical potential long-term issues. However they didn’t understand it but, as a result of the AI stored producing reasonable-looking solutions. As they added extra knowledge sources over the next weeks, the AI began taking longer to reply. Hallucinations crept in that they couldn’t monitor right down to any particular knowledge supply. What had been a extremely priceless instrument grew to become a bear to keep up.
The group had fallen into the knowledge hoarding lure: Their early fast wins created a tradition the place individuals simply threw no matter they wanted into the context, and finally it grew right into a upkeep nightmare that solely acquired worse as they added extra knowledge sources.
The Expertise That By no means Develop
There are as many opinions on knowledge structure as there are builders, and there are often some ways to resolve anyone drawback. One factor that nearly everybody agrees on is that it takes cautious selections and plenty of expertise. Nevertheless it’s additionally the topic of numerous debate, particularly inside groups, exactly as a result of there are such a lot of methods to design how your utility shops, transmits, encodes, and makes use of knowledge.
Most of us fall into just-in-case pondering at one time or one other, particularly early in our careers—pulling all the info we’d probably want simply in case we’d like it reasonably than fetching solely what we’d like once we really need it (which is an instance of the alternative, just-in-time pondering). Usually once we’re designing our knowledge structure, we’re coping with instant constraints: ease of entry, dimension, indexing, efficiency, community latency, and reminiscence utilization. However once we use MCP to offer knowledge to an AI, we are able to usually sidestep lots of these trade-offs…quickly.
The extra we work with knowledge, the higher we get at designing how our apps use it. The extra early-career builders are uncovered to it, the extra they study by way of expertise why, for instance, System A ought to personal buyer standing whereas System B owns fee historical past. Wholesome debate is a crucial a part of this studying course of. By means of all of those experiences, we develop an instinct for what “an excessive amount of knowledge” seems like—and the best way to deal with all of these difficult however vital trade-offs that create friction all through our initiatives.
MCP can take away the friction that comes from these trade-offs by letting us keep away from having to make these selections in any respect. If a developer can wire up all the pieces in only a few minutes, there’s no want for dialogue or debate about what’s really wanted. The AI appears to deal with no matter knowledge you throw at it, so the code ships with out anybody questioning the design.
With out all of that have making, discussing, and debating knowledge design selections, builders miss the possibility to construct vital psychological fashions about knowledge possession, system boundaries, and the price of shifting pointless knowledge round. They spend their adolescence connecting as a substitute of architecting. That is one other instance of what I name the cognitive shortcut paradox—AI instruments that make growth simpler can stop builders from constructing the very expertise they should use these instruments successfully. Builders who rely solely on MCP to deal with messy knowledge by no means study to acknowledge when knowledge structure is problematic, similar to builders who rely solely on instruments like Copilot or Claude Code to generate code by no means study to debug what it creates.
The Hidden Prices of Information Hoarding
Groups use MCP as a result of it really works. Many groups fastidiously plan their MCP knowledge structure, and even groups that do fall into the info hoarding lure nonetheless ship profitable merchandise. However MCP continues to be comparatively new, and the hidden prices of information hoarding take time to floor.
Groups usually don’t uncover the issues with a knowledge hoarding strategy till they should scale their functions. That bloated context that hardly registered as a price to your first hundred queries begins displaying up as an actual line merchandise in your cloud invoice whenever you’re dealing with hundreds of thousands of requests. Each pointless subject you’re passing to the AI provides up, and also you’re paying for all that redundant knowledge on each single AI name.
Any developer who’s handled tightly coupled lessons is aware of that when one thing goes mistaken—and it all the time does, finally—it’s lots tougher to debug. You usually find yourself coping with shotgun surgical procedure, that actually disagreeable state of affairs the place fixing one small drawback requires modifications that cascade throughout a number of components of your codebase. Hoarded knowledge creates the identical form of technical debt in your AI programs: When the AI offers a mistaken reply, monitoring down which subject it used or why it trusted one system over one other is tough, usually not possible.
There’s additionally a safety dimension to knowledge hoarding that groups usually miss. Every bit of information you expose by way of an MCP instrument is a possible vulnerability. If an attacker finds an unprotected endpoint, they’ll pull all the pieces that instrument supplies. If you happen to’re hoarding knowledge, that’s your complete buyer database as a substitute of simply the three fields really wanted for the duty. Groups that fall into the info hoarding lure discover themselves violating the precept of least privilege: Functions ought to have entry to the info they want, however no extra. That may convey an unlimited safety threat to their complete group.
In an excessive case of information hoarding infecting a complete firm, you may uncover that each group in your group is constructing their very own blob. Assist has one model of buyer knowledge, gross sales has one other, product has a 3rd. The identical buyer seems utterly totally different relying on which AI assistant you ask. New groups come alongside, see what seems to be working, and duplicate the sample. Now you’ve acquired knowledge hoarding as organizational tradition.
Every group thought they have been being pragmatic, transport quick, and avoiding pointless arguments about knowledge structure. However the hoarding sample spreads by way of a corporation the identical means technical debt spreads by way of a codebase. It begins small and manageable. Earlier than you understand it, it’s in every single place.
Sensible Instruments for Avoiding the Information Hoarding Entice
It may be actually tough to educate a group away from knowledge hoarding after they’ve by no means skilled the issues it causes. Builders are very sensible—they wish to see proof of issues and aren’t going to sit down by way of summary discussions about knowledge possession and system boundaries when all the pieces they’ve performed to this point has labored simply high-quality.
In Studying Agile, Jennifer Greene and I wrote about how groups resist change as a result of they know that what they’re doing right now works. To the particular person attempting to get builders to alter, it might look like irrational resistance, nevertheless it’s really fairly rational to push again towards somebody from the surface telling them to throw out what works right now for one thing unproven. However similar to builders finally study that taking time for refactoring speeds them up in the long term, groups have to study the identical lesson about deliberate knowledge design of their MCP instruments.
Listed here are some practices that may make these discussions simpler, by beginning with constraints that even skeptical builders can see the worth in:
- Construct instruments round verbs, not nouns. Create
checkEligibility()orgetRecentTickets()as a substitute ofgetCustomer(). Verbs pressure you to consider particular actions and naturally restrict scope. - Speak about minimizing knowledge wants. Earlier than anybody creates an MCP instrument, have a dialogue about what the smallest piece of information they should present for the AI to do its job is and what experiments they’ll run to determine what the AI really wants.
- Break reads other than reasoning. Separate knowledge fetching from decision-making whenever you design your MCP instruments. A easy
findCustomerId()instrument that returns simply an ID makes use of minimal tokens—and won’t even should be an MCP instrument in any respect, if a easy API name will do. ThengetCustomerDetailsForRefund(id)pulls solely the precise fields wanted for that call. This sample retains context centered and makes it apparent when somebody’s attempting to fetch all the pieces. - Dashboard the waste. The most effective argument towards knowledge hoarding is displaying the waste. Monitor the ratio of tokens fetched versus tokens used and show them in an “data radiator” fashion dashboard that everybody can see. When a instrument pulls 5,000 tokens however the AI solely references 200 in its reply, everybody can see the issue. As soon as builders see they’re paying for tokens they by no means use, they get very taken with fixing it.
Fast odor check for knowledge hoarding
- Device names are nouns
(getCustomer())as a substitute of verbs(checkEligibility()). - No one’s ever requested, “Do we actually want all these fields?”
- You’ll be able to’t inform which system owns which piece of information.
- Debugging requires detective work throughout a number of knowledge sources.
- Your group not often or by no means discusses the info design of MCP instruments earlier than constructing them.
Trying Ahead
MCP is an easy however highly effective instrument with huge potential for groups. However as a result of it may be a critically vital pillar of your complete utility structure, issues you introduce on the MCP degree ripple all through your mission. Small errors have big penalties down the street.
The very simplicity of MCP encourages knowledge hoarding. It’s a simple lure to fall into, even for knowledgeable builders. However what worries me most is that builders studying with these instruments proper now may by no means study why knowledge hoarding is an issue, they usually gained’t develop the architectural judgment that comes from having to make arduous selections about knowledge boundaries. Our job, particularly as leaders and senior engineers, is to assist everybody keep away from the info hoarding lure.
Once you deal with MCP selections with the identical care you give any core interface—preserving context lean, setting boundaries, revisiting them as you study—MCP stays what it must be: a easy, dependable bridge between your AI and the programs that energy it.
