One moment you're speaking about context but talking in kilobytes, can you confirm the token savings data?

And when you say only returns summaries, does this mean there is LLM model calls happening in the sandbox?

For your second question: No LLM calls. Context Mode uses algorithmic processing — FTS5 indexing with BM25 ranking and Porter stemming. Raw output gets chunked and indexed in a SQLite database inside the sandbox, and only the relevant snippets matching your intent are returned to context. It's purely deterministic text processing, no model inference involved.

Excellent, thank you for your responses. Will be putting it through a test drive.

Looks pretty interesting. How could i use this on other MCP clients e.g OpenCode ?

Hey! Thank you for your comment! You can actually use an MCP on this basis, but I haven't tested it yet. I'll look into it as soon as possible. Your feedback is valuable.

nice, I'd love to se it for codex and opencode

Nice trick. I’m going to see how I can apply it to tool calls in pi.dev as well

Really cool. A tangential task that seems to be coming up more and more is masking sensitive data in these calls for security and privacy. Is that something you considered as a feature?

Good question.

The SQLite database is ephemeral — stored in the OS temp directory (/tmp/context-mode-{pid}.db) and scoped to the session process. Nothing persists after the session ends. For sensitive data masking specifically: right now the raw data never leaves the sandbox (it stays in the subprocess or the temp SQLite store), and only stdout summaries enter the conversation. But a dedicated redaction layer (regex-based PII stripping before indexing) is an interesting idea worth exploring. Would be a clean addition to the execute pipeline.

Nothing persists after the session ends.

Does that mean that if I exit claude code and then later resume the session, the database is already lost? When exactly does the session end?

Interesting approach, I tried the Hackernews example from the docs, but its tools don't seem to trigger reliably. Any suggestions?

Fetch the Hacker News front page, extract all posts with titles, scores, and domains. Group by domain. Then run /context-mode stats.

* Claude used regular fetch *

why didnt you use the context mode fetch?

● Fair point. Two honest reasons:

  1. First request: The context-mode tools weren't loaded yet when I called WebFetch. I should have used ToolSearch to load    
  fetch_and_index first, then used it — but I defaulted to WebFetch out of habit.                                              
  2. Second request (the interrupted one): The tools were already loaded at that point. I had no excuse. I should have done:   
  fetch_and_index(url: "https://news.ycombinator.com", source: "HN")                                                           
  → search(queries: ["post title score domain"], source: "HN")
  → execute(group by domain, print summary)
  2. Instead I reached for WebFetch again — that's the anti-pattern the plugin exists to prevent.

  The context-mode skill instructions explicitly list this in the decision tree:

  Fetching web documentation or HTML page? → Use fetch_and_index → search

  I ignored it. The WebFetch output (the full post table) went straight into context when it didn't need to. I'll use
  fetch_and_index correctly if you want to retry.

Im not sure i understand how it coexists with existing installed MCP servers

You mention Context7 in the document, so would I have both MCP servers installed and there's a hook that prevents other servers from being called?

Interesting approach. I just finished some work for a similar task in a different domain.

One thing that surprised me: tantivy's BM25 search is faster, more expressive, and more scalable than SQLite. If you're just building a local search (or want to optimize for local FTS), I would strongly recommend looking into tantivy.

If you have the resources, it would be very interesting to throw a some models (especially smart-but-context-constrained cheaper ones) at some of the benchmark programming problems and see if this approach can show an effective improvement.