Onsa

Starts a live B2B lead search with Onsa's agent, matching real people (with LinkedIn profiles) against the workspace's ICP. Takes a natural-language brief - titles, company type, geography, e.g. 'find 5 fintech founders in NYC'. Returns a jobId immediately; the search itself usually takes 3-10 minutes, and its status and results are read with fetch_leads. `limit` is a target the agent aims at rather than a cap, so it often returns more than asked. Every lead it finds counts against the workspace's prospect allowance.

Returns the status and any results of a find_leads job, by jobId. Status values: "pending" - the search is still running, though `leads` may already hold a partial list; "completed" - the agent delivered a batch, which is not a guarantee that no more will arrive, and `total` may still rise; "stalled" - no leads arrived for about 15 minutes, meaning the agent asked a question, errored, or never delivered. Also returns `total` (leads in this response), `campaignId` (accepted by get_campaign, get_campaign_leads and get_campaign_stats) and `campaignUrl`, a deep link to the prospects tab for this search in Onsa. `agentMessage` is agent text from this campaign's chat, filtered to what was said after this job started, so it never predates the request - though on a campaign with several searches i

Lists the campaigns (past lead searches) in this workspace that the user takes part in, newest first. Returns the newest `limit` of them, default 50; `returned` against `total` shows whether older campaigns were omitted. Each entry has id, title, leadsTotal, hasIcp, tags, createdAt and updatedAt. An id is accepted by get_campaign for its ICP, get_campaign_leads for its people, get_campaign_stats for its outreach funnel, list_replies for what prospects wrote back, and list_next_steps for what the campaign still needs a human to do. Searches started over MCP often share a generic title, so the ICP and the dates distinguish cohorts more reliably than the title alone. A campaign row exists from the moment a search starts, so the newest entry is frequently still empty, with leadsTotal 0.

Returns one campaign's ICP - the ideal-customer profile the agent derived and scores leads against - plus its outreach template and settings. The ICP comes back exactly as stored, in snake_case: `perfect_lead` and `reachable_market` are one-line summaries, while `company` and `person` hold the rules that actually drive scoring, each an object with `critical` and `preferential` rule lists. `product` and `owner` describe the seller. The two summary strings are not the scoring criteria; `company.critical` and `person.critical` are. No key is guaranteed present. `outreachTemplate` shows how much personalization the messages allow: a template whose only placeholders are [FIRST_NAME] and [COMPANY_NAME] produces near-identical mail-merge copy for every lead.

Returns the leads of any campaign by campaignId, with the same fields as fetch_leads, including score and scoreExplanation. It covers campaigns not started in this session, which fetch_leads cannot reach because fetch_leads requires a jobId from a find_leads call in the same session. Passing `leadIds` resolves the reply buckets from get_campaign_stats back into named people.

Sends an instruction to the agent inside an existing campaign and returns a jobId to read with fetch_leads. This is the tool that grows or steers a cohort in place - 'find 5 more like these', 'look at Singapore and the Gulf instead of US institutions', 'focus on funds over $5bn AuM'. find_leads always creates a separate campaign with its own ICP, which splits the funnel and leaves the two cohorts incomparable. The agent sees the campaign's existing leads and ICP, so they can be referred to. It cannot answer back through this API, so a question sent here gets no response. New leads count against the prospect allowance, and de-duplication is per workspace, so a request for 5 more can yield fewer when the agent rediscovers people already in the workspace. It does not remove or skip leads: 'dr

Lists outreach messages the agent has drafted that are waiting for a human to approve - the 'a message for X is ready' queue. Each entry carries the draft text, the lead it is for, and why that lead scored as it did. Omitting campaignId covers the whole workspace. This tool sends nothing: rewrite_outreach replaces a draft's text, and send_outreach queues one for delivery.

Returns the research memo Onsa's agent wrote about one lead: role history, company size and stage, what they have said publicly, and the angle on them. It is usually far richer than scoreExplanation, and it is the source material for outreach built on a specific, checkable fact rather than a generic opener. Most leads have no memo - Onsa writes one only for prospects it has researched - so `memo: null` is the common case and not an error, and scoreExplanation is the remaining source in that case.

Replaces the text of an outreach draft that is waiting for approval. The current draft and the lead's scoreExplanation come from list_pending_outreach; get_lead_memo carries the richer research on that person. The rewritten draft stays in the approval queue, and this tool sends nothing.

Queues one already-approved outreach draft for delivery to a real person on LinkedIn. It requires `confirmText`, the draft body character-for-character as stored, and `confirmName`, the recipient's name: drafts are often near-identical between people, so matching the body alone does not identify which one was meant. A mismatch is refused without returning the stored text, which list_pending_outreach supplies. The server additionally requires a confirmation from the person at the keyboard, rendered by the MCP client and quoting the draft as stored; that approval is single-use and bound to one recipient and one draft. A client that cannot render such a confirmation receives a refusal carrying a link to approve inside the Onsa app, and nothing is queued. On success the message is queued rathe

Returns the text of what prospects replied, for every lead in the campaign that answered, paired with the outbound message it answers. get_campaign_stats counts replies and labels them; this returns the words. `sentiment` is Onsa's own label, written once per lead on their first reply - later replies never change it, and a reply Onsa has not scored yet comes back as `sentiment: null`, which means unscored rather than neutral. Those unscored replies are absent from get_campaign_stats entirely, so this tool can return more replies than the funnel counts. Three derived fields come with the reply. `awaitingOurReply`: the prospect spoke last and no sent message followed. It is structural only - a flat 'no thanks' satisfies it too - and Onsa sees only what Onsa sent, so a reply made by hand ins

Returns what this campaign still needs from a human, as a ranked to-do list: people who replied, people who accepted an invite but were never messaged, drafts waiting for approval, leads found but never contacted, and setup that is missing. It answers 'what should I do about this campaign today', where get_campaign_stats answers 'how is it doing'. For replies specifically, list_replies is the better source: its `awaitingOurReply` compares timestamps, while the `replied` bucket here covers only leads whose sentiment was scored and does not know whether we have since answered. `leadIds` passed to get_campaign_leads resolves the buckets into named people. Limits of the data: Onsa does not record whether we have already replied, or whether someone was contacted outside Onsa; 'replied' includes