June 2026 · 7 min read

Claude Context Window Full? Stop Losing Work Mid-Conversation

You're deep into a long Claude session — a document review, a research sprint, a complex piece of code — and it happens. The responses start getting vaguer. Claude loses the thread. Sometimes it tells you the context is getting large. Sometimes it just quietly starts forgetting what you told it two hours ago.

This is the context window problem. It's not a bug. It's a fundamental constraint of how large language models work. And most people try to solve it with the wrong tools.

Why Claude's context window fills up

Claude's context window is a fixed amount of text it can hold "in mind" at once — everything from your system prompt, to the conversation history, to the documents you've pasted in. When that budget runs out, older content gets pushed out.

The practical limit for most Claude plans is somewhere between 100,000 and 200,000 tokens. That sounds like a lot until you're doing something real: a 50-page document plus a long back-and-forth conversation can easily fill 80,000 tokens before you're halfway done.

And when it fills up, you have three options — all of them annoying:

1. Start a new conversation. You lose all the context you built up. You have to explain everything again from scratch.
2. Copy-paste the important stuff into a new chat. This is tedious and error-prone. You always miss something.
3. Paste everything into your system prompt. But system prompts have their own character limits, and front-loading all your context costs tokens on every single message.

None of these is a real fix. They're workarounds that shift the pain around without eliminating it.

The actual fix: pull context on demand

The real solution is a different architecture: instead of pushing all your context into Claude at the start and watching it get consumed, you let Claude pull exactly what it needs, exactly when it needs it.

This is what MCP connectors make possible. An MCP (Model Context Protocol) connector is a URL you add to Claude's settings once. After that, Claude can call tools on your connector mid-conversation — searching a record store, loading a note, retrieving a saved context — without that data eating into your system prompt or your token budget unnecessarily.

Stash is a hosted MCP connector built for exactly this use case.

How Stash solves the context window problem

Stash has a tool called context(). It's a standing context loader — you store who you are, what you're working on, how you like Claude to respond, and any other standing facts in a Stash collection called context. When you start a new conversation and call context(), Claude loads exactly those records — and nothing else.

The result: a 200-token summary of your standing context instead of a 4,000-token system prompt paste. You start fresh without starting from scratch.

For longer sessions, you can store intermediate work in Stash mid-conversation, start a new chat when the context fills up, and have Claude pull exactly what it needs to continue. No copy-paste. No lost threads.

That second response came from a search() call — Claude searched your Stash records for "Meridian filter" and pulled the relevant note. The context window stayed clean. No manual paste required.

The workflow: using Stash to manage long sessions

Here's the pattern once you have Stash connected:

1. At the start of any session: Call context(). Claude loads your standing facts (role, projects, preferences) in a few hundred tokens.
2. Mid-session, when you hit something worth keeping: Ask Claude to store it. "Add this decision to my Meridian notes in Stash." Done — it's saved, searchable, and available in any future session.
3. When the context window starts filling up: Start a new conversation. Call context() again. Ask Claude to search for whatever it needs to continue. Your work is never lost.
4. For deep dives: Load only what's relevant. "Search Stash for anything about the Meridian sidebar" — Claude gets exactly those records, not your entire history.

The key difference from a system prompt or a paste: you're not front-loading everything. You're letting the conversation breathe, and pulling context in where it's actually needed.

Comparison: the old way vs MCP context management

Setup takes 30 seconds

Stash is a hosted MCP connector — no install, no server to manage. The entire setup:

1. Sign in with Google at stashlite.com — you get a connector URL instantly.
2. In Claude: Settings → Connectors → Add custom → paste the URL → sign in with Google.
3. In any Claude chat: type context() to load your standing context.

Free tier includes 2,500 records and 50 queries per month. Enough to manage context across a full month of active work without paying anything.

When this matters most

The context window problem is most painful for:

• Long research sessions — synthesising papers, building a literature review, tracking what you've read and what conclusions you've drawn.
• Ongoing projects — any work that spans multiple days or conversations, where you need continuity without pasting history every time.
• Client work — keeping track of decisions, preferences, and constraints per client, loadable on demand.
• Anything with a decision log — architecture choices, writing decisions, design rationale. Searchable, persistent, and never lost to a context flush.

The context window will always be finite. The question is whether you fight it with paste-and-pray, or manage it properly with a retrieval layer that costs 200 tokens instead of 4,000.

Further reading

• Claude memory via MCP: how it works under the hood
• The Start My Day pattern: loading context in one call
• Claude Projects vs MCP connectors: which one actually persists?
• How to save information in Claude across conversations