Model Context Protocol
Your work context, as an MCP server
The problem MCP actually solves
Every model you use starts each conversation with amnesia. You compensate by pasting: the thread where the decision was made, the ticket, last week's call notes, the bit of the doc that matters.
That paste is the whole job. It is also the reason your AI tools stay shallow — you only ever give them the context you remembered to find, which is a fraction of what exists and skewed toward whatever you looked at most recently.
The Model Context Protocol exists so tools can fetch context for themselves. Jarvis is the server on the other end of that connection: instead of you searching Slack and pasting the result, the model issues search_context and gets it.
What it looks like in practice
A real exchange, in Claude Code. You ask a question that has no answer in the repository, because the answer lives in the last three days of your team's conversation:
❯ what did I miss on the payments bug?
jarvis － search_context(query: "payments bug")
└ 12 messages across 4 sources
Tom's fix is in review, not shipped. It double-charged 3 customers.
#eng · 3d · ENG-2481 · PR #142 · Eng sync, Aug 19
Four sources, one question, no pasting. The citations matter as much as the answer — you can check the claim rather than trust it.
The tools the server exposes
Six retrieval tools, deliberately narrow. Each returns cited results with a source and a timestamp, so the model can attribute what it tells you and you can follow it back.
search_context
Full-text and semantic search across every synced source at once. One query, ranked results, with the source and date attached.
get_thread
The whole conversation a message belongs to, so an answer carries the argument that produced it rather than one line out of context.
get_surrounding_messages
The messages either side of a hit. Cheap way to widen a result without pulling an entire channel into the model's context window.
get_messages_on_date
Everything from a given day across all sources — the query behind "what did I miss on Tuesday?"
list_conversations
The channels, DMs, and threads available to search, so a client can scope a query before running it.
get_conversation_messages
A single conversation, read in order, paginated.
Why the store is local
Your inbox does not need another cloud copy
Syncing years of Slack and email into someone else's infrastructure is a real decision with real consequences, and it is the reason most people never adopt a tool like this. Jarvis keeps the index on your machine.
Retrieval is a local read
Queries resolve in milliseconds against a local store, and they work on a plane. There is no round trip to a vector database you are renting by the month.
Personal context stays personal
Jarvis keeps the context you accumulate separate from what your team shares. Your DMs and your inbox inform your answers without becoming company-wide knowledge, which is the distinction most "team knowledge base" products collapse.
You can point it at any client
MCP is a protocol, not an integration. Anything that speaks it — Claude Code, Claude Desktop, your own agent — connects the same way.
How this differs from a RAG pipeline
| Rolling your own RAG | Jarvis | |
|---|---|---|
| Getting the data in | Write and maintain a connector per source, plus the auth, backfill, and incremental sync for each. | Slack, Gmail, and meeting notes sync out of the box. Adding a source is a setting, not a sprint. |
| Keeping it current | A scheduled re-index you own, which silently goes stale when a token expires. | Continuous sync. The store reflects this morning's threads, not last month's snapshot. |
| Where it runs | Usually a hosted vector database, billed monthly, holding a copy of your inbox. | On your machine. Nothing leaves it to answer a query. |
| Wiring it to a model | A bespoke tool definition per client, rewritten when the client changes. | One MCP endpoint. Any MCP client connects without new code. |
Rolling your own RAG details last checked August 2026. These products ship quickly — check their site for anything that matters to your decision.
What this is not
This is not a general-purpose document store, and it is not a replacement for search over your codebase — your repository is already a better index of itself than any of this.
It is specifically the conversational context that lives outside your code: what was decided, by whom, in which thread, and what was still unresolved when the meeting ended.
Getting access
Jarvis is in early access and we are onboarding design partners a few at a time — developers using Claude Code daily are exactly who we want on it now. Join the waitlist and tell us what you would want it to answer.
If you want the broader picture first, how Jarvis handles context generally covers what it syncs and how the layers separate.
Give your AI tools your context
Jarvis is a Mac app. Connect your chats, email and calendar, and it tells you what to do first.
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