What "Context" Means When Every Tool Claims It

A coding assistant vendor says their model has a bigger context window. A retrieval product says it gives your agent context by connecting to your docs. An MCP server says it hands your agent context straight from Slack and Linear. All three are using the same word for genuinely different things, and only one of them tells you anything about whether what the agent reads is actually still true.

Context means the current, checked answer to “what’s actually true and decided here,” not a pile of raw messages or files. A tool that reads your Slack has data. A tool that knows which of three conflicting messages is the real decision has context. That distinction sounds pedantic until you’re the one deciding what an agent should trust before it ships something.

What does “context” actually mean, when every tool claims to have it?

It depends entirely on which of three genuinely different things a given vendor is calling context, and most pitches don’t tell you which one you’re getting. “Context window” describes how much raw text a model can take in as input at once, a property of the model, not a claim about whether any of that text is current. Raw data access, whether through an MCP connection, a retrieval index, or a plain API integration, describes how much of your company’s tools an agent can technically reach, not whether what it reaches is the version your team actually stands behind today. And a checked, current answer describes something else again: a record that’s been reconciled against competing versions of the truth, so an agent reading it isn’t just seeing more, it’s seeing the one thing that’s actually settled.

Those three things get marketed under the identical word constantly, and the difference between them is the entire question that matters when you’re deciding whether to trust an agent’s output.

Does a bigger context window or more connected tools actually solve this?

No, because both give an agent more raw material to read, not more certainty about which of it is still accurate. A model with a much larger context window can ingest an entire Slack channel’s history, every version of a spec, and a dozen related tickets in one pass. That’s a real capability. It still doesn’t tell the model which of those documents reflects the team’s current thinking and which one got overruled in a meeting two days after it was written. More room to read is not the same problem as knowing what’s worth trusting once you’ve read it.

The same logic holds for wiring an agent up to more tools. An MCP connection to Slack, GitHub, and Linear gives an agent the ability to fetch data from all three. It doesn’t give the agent a way to know that the Linear ticket it just fetched is three days behind a decision that already happened in Slack. Access and currency are separate axes entirely, and adding more of one does nothing to the other.

Why does “context equals more data” fail in practice?

Because conflicting information can sit “in context” simultaneously with no signal attached telling an agent which piece actually wins. This is the exact failure this site has already walked through in detail with a specific example, and it’s worth reusing here rather than inventing a new one, because it demonstrates precisely the gap between raw access and a checked answer.

A ticket says a discount caps at 20%. A Slack thread from two days later says the team walked that back to 15% after finance flagged a margin problem. Both of those are technically “in context” for an agent with access to the tracker and the Slack workspace. Nothing about having access to both tells the agent that the Slack message is the newer, superseding decision and the ticket just hasn’t caught up. An agent with a huge context window and a wide-open MCP connection can have both facts sitting right in front of it and still confidently build against the wrong one, because volume of access was never the part that was missing.

That’s the practical shape of the failure: not that an agent lacks data, but that it lacks a signal for which piece of data is current when two pieces disagree. Ticket versus Slack thread conflicts are common precisely because nothing in most tool stacks resolves that disagreement automatically; it just sits there, technically visible, functionally ambiguous.

What does Thor mean by “context” specifically?

A living model of what’s true, sourced and governed, not a bigger pile of raw material to search through. Thor is the context infrastructure for AI-native software teams, a living model of what’s true that turns conversation into approved action. The word “living” is doing real work in that definition: it’s not a snapshot taken once and left to age, it’s a record that gets updated as decisions actually change, with a visible source for each fact and a person confirming anything that creates a real commitment.

That’s a structurally different thing from a data lake or a search index. A data lake answers “what documents exist that mention this topic.” Thor’s living model answers a narrower, harder question: of everything that’s been said about this, what’s the current, settled version, and who or what established it. The first is a retrieval problem. The second is closer to reconciliation, and it’s the part that actually determines whether an agent’s output is trustworthy.

What does the difference look like concretely?

Take that same discount-ticket scenario through both versions side by side.

Both columns technically involve “context” in the loose sense every vendor uses. Only the second column answers the question that actually matters before code ships.

Why does this distinction matter for a buying decision?

Because “how much data can your tool see” is the wrong question, and most vendor conversations default to asking exactly that one. A team evaluating an “AI context” product should be asking what happens the moment two sources disagree, since that’s the situation where raw access and a checked answer produce completely different outcomes. A tool that can only say “I can retrieve documents from all your systems” hasn’t actually answered that question. A tool that can say “here’s what happens when a ticket and a Slack decision conflict, and here’s who resolves it” has.

Research on how language models get used across fact-checking and verification workflows draws a related distinction worth borrowing here: retrieving relevant text and confirming that text is actually correct or current are two separate steps, and a system built only for the first step will confidently surface information that the second step would have caught as outdated or contradicted. Context products are no exception. Retrieval is the easy half of the problem. Reconciling conflicting versions of the truth is the half that actually determines whether an agent ships the right thing.

That’s also why “we’ll just wire everything through MCP” undersells what’s actually needed. MCP alone doesn’t solve this: it’s a way to fetch data from a tool, not a way to know whether the data it fetches is the current, agreed-upon version. A wider MCP surface gives an agent more places to look. It doesn’t give the agent, or the person evaluating the tool, an answer to the question that was actually being asked.

FAQ

Isn’t a bigger context window basically the same thing as more context?

No. A context window measures how much text a model can process in one pass. It says nothing about whether any of that text reflects the team’s current, settled thinking. You can max out a huge window entirely with stale information.

Does connecting an agent to more tools via MCP count as giving it context?

It gives the agent more places to fetch data from. Whether that data is current, and which version wins when two sources disagree, is a separate question MCP access alone doesn’t answer.

What’s the actual test for whether a tool provides real context, not just data access?

Ask what happens when two sources disagree. A tool that can only retrieve documents has no answer. A tool that can say which one is current, sourced to a specific decision, does.

Is Thor a retrieval or search tool?

No. Retrieval finds relevant documents. Thor’s job is reconciling what’s actually true when documents disagree, and keeping that answer current as decisions change.

Does more context always mean better agent output?

Not automatically. More raw material without a way to tell which of it is current can produce a more confidently wrong answer just as easily as a better one. What actually improves output is knowing which piece of context to trust.

See how your agents work from one checked answer instead of a stale ticket. Book a demo.