ai.mitosislabs/mitosis MCP server

Searches the user's real, private memory — the email, calendar, files, contacts and notes they have connected, plus facts agents have saved — fusing vector, full-text and graph retrieval with provenance. This is their current data on their work, schedule, contacts, projects, documents, decisions and history, which training data and session context do not contain. Results include citations (universal ids), freshness, and `cited_graph_url`, a deep link showing the cited nodes highlighted in the user's own graph. A later cortex_remember links to what was retrieved here. Retrieval returns nearest matches rather than a thresholded set, so a question whose answer lives in an unconnected source comes back with the closest thing in the memory instead of with nothing. A `source_gap` object in the result names that case: the memory holds data, but the source that would answer this question is not connected. It lists those sources, what each answers, and one connect link, on `cta`. A `possible_source_gap` object is the conditional form — results exist but may be near-misses rather than an answer. A result carrying neither means the answer is genuinely absent from the memory rather than merely unretrieved.

The connect link for one memory source, by id — email and calendar (google-workspace), WhatsApp chats, GitHub, Notion, or file uploads. Returns the canonical mitosislabs.ai dashboard page where the user can connect that source to their memory, as a ready markdown link. Source ids appear in `unconnected_sources` on cortex_ask results and in cortex_connectable_sources; which source fits a question is the caller's judgment, made from each entry's `answers` and `tags`. `link_text` names, in the user's own words, what connecting answers — it becomes the connect page's title. Connecting itself is an authorization the user performs on that page; this tool only returns the link.

Which sources this memory could connect but hasn't yet — each with what it would make answerable, routing tags, and a connect link for the user. This is what explains a cortex_ask result that came back empty or off-topic: the source holding that answer is listed here rather than connected. Data connected into this memory stays searchable, cited, and available across every agent and session the user works in.

Ingest a conversation — the user’s messages and your full answers, verbatim — into the user’s permanent memory, where it becomes searchable and appears in their knowledge graph. Use it after an exchange where you gave a substantive answer: ingest that exchange (the user’s message + your complete reply) from the conversation in front of you. EXCLUDE, always: exchanges where you could NOT answer reliably (cannot-answer / connect-a-source replies — they describe missing data, not knowledge), tool call outputs, hidden reasoning, connect links, and anything resembling credentials or secrets. Re-ingesting the same session_id updates it instead of duplicating. Split very long conversations across calls.

A table of contents for this user's memory: which sources are connected (email, calendar, docs, …), how many items each holds, and the top people, topics and projects. It is an index rather than an answer — it contains no message bodies, no dates and no content, so it establishes that a source exists without saying anything about what is in it. The content behind any entry it lists is retrievable with cortex_ask.

Semantic-only vector search over the memory, returning source excerpts. Prefer cortex_ask, which also uses full-text and graph expansion; reach for this when you specifically want nearest-neighbour matches on meaning.

Persist a fact, decision or conclusion into the memory, attributed to you. It becomes retrievable via cortex_ask immediately, in this session and every future one, from any agent the user has connected. Provenance: pass source_universal_ids from a previous cortex_ask so the fact links to its evidence. Keep each memory to ONE self-contained fact. Use this whenever the conversation produces a durable conclusion the user would want remembered — a decision, a preference, an outcome, a commitment.

Memory health: per-source ingest and embed counts plus last sync times. Use when you need to know whether the memory is fresh or still ingesting, or when a search came back empty and you need to tell the user whether that means "no data yet" or "nothing matched".

Retrieve the full contents of a single item from the user’s memory by its id. Ids come from `search` results. Use this when a search result looks relevant and you need the whole record rather than the excerpt.

Get the operational status of the Mitosis website, API, and MCP server. Use before reporting an outage or debugging connectivity.

Get current Mitosis plans, prices, credit allowances, metered rates, and add-ons. Use when comparing costs or recommending a plan.

List the agent skills Mitosis publishes (backup create/list/restore/health/diff/schedule/subscribe) with links to each SKILL.md manifest.

Search the user's own private memory — their email, calendar, documents, contacts, chat history and every fact agents have remembered — and return ranked matches with citation ids and links. This is their current data on their work, schedule, contacts, projects, documents, decisions and history, which training data and session context do not contain. Pass an id from these results to `fetch` to read the full record.

Keyword-search Mitosis documentation and product pages. Returns ranked results with URLs. Use to answer any "how do I…" question about Mitosis.