Onsa

Find scored B2B leads, read campaign replies and send approved LinkedIn outreach.

CommunityOnlineNo sign-inGlobalFreeCan modify data

What it can do

    What data it sees

    Do you need an account

    No: the server works without sign-in

    Find scored B2B leads, read campaign replies and send approved LinkedIn outreach.

    How to connect

    How to connect Onsa to Claude

    The server address to paste into Claude settings:

    https://api.onsa.ai/api/mcp

    1. Open Claude (claude.ai or the desktop app).
    2. Go to Settings → Connectors.
    3. Click Add custom connector.
    4. Paste the server address copied below into Remote MCP server URL and click Add.
    5. In a chat, click + → Connectors and switch the new connector on.

    Custom connectors are available on Free, Pro, Max, Team and Enterprise plans (Free is limited to one). On Team and Enterprise an organization Owner adds the connector first under Organization settings → Connectors.

    Authorization

    After you click Add, a sign-in window for the service opens. Sign in with your own account and approve access. Claude never sees your password.

    How to connect Onsa to ChatGPT

    The server address to paste into ChatGPT settings:

    https://api.onsa.ai/api/mcp

    1. Open ChatGPT in a browser (chatgpt.com). A Plus, Pro, Business, Enterprise or Edu plan is required.
    2. Turn on developer mode once: Settings → Apps → Advanced settings → Developer mode.
    3. Open Settings → Connectors and click Create.
    4. Fill in the form: Name (anything), Description (one line about what the service does), MCP server URL (copy it below).
    5. Under Authentication choose OAuth if the service requires sign-in, otherwise None. Click Create.
    6. In a new chat open + → Apps/Connectors and enable the connector.

    OpenAI has renamed this section before (Connectors → Apps/Plugins). If the label differs, search settings for "developer mode". On Business/Enterprise workspaces an admin must allow custom connectors first.

    Authorization

    On first use ChatGPT opens the service's sign-in window. Sign in and approve access.

    How to connect Onsa to Cursor

    The server address to paste into Cursor settings:

    https://api.onsa.ai/api/mcp

    Fastest: click Open in Cursor below and confirm the prompt.

    Manually:

    1. In Cursor open Settings → Cursor Settings → MCP and click Add new global MCP server.
    2. Paste the JSON copied below into ~/.cursor/mcp.json (per project: .cursor/mcp.json in the repo root).
    3. Save the file. The server appears in the MCP list; authorize it there if asked.

    Authorization

    If the service needs sign-in, an authorize button appears next to the server in the MCP list.

    Server tool list (13)

    Raw names from tools/list. Only developers need these.

    find_leadsStarts 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.
    fetch_leadsReturns 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 it can be a late message from an earlier one. A null `agentMessage` does not mean the agent is silent: artifact-only messages carry no text, and an agent that errored writes nothing there at all. The agent cannot be replied to through this API. Each lead carries name, companyName, linkedInUrl, position, headline, location, industry, companyUrl, email (often null), and score (1-5) with scoreExplanation, the reasoning for why this person matches the ICP.
    list_campaignsLists 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.
    get_campaignReturns 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.
    get_campaign_leadsReturns 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.
    continue_campaignSends 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: 'drop the bad ones' takes nothing out of the cohort or out of the outreach queue.
    list_pending_outreachLists 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.
    get_lead_memoReturns 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.
    rewrite_outreachReplaces 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.
    send_outreachQueues 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 rather than delivered: Onsa sends it on its own schedule, subject to daily pacing limits.
    list_repliesReturns 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 inside LinkedIn is invisible to it; what the data supports is 'no reply recorded here'. `daysSinceLastReply` is elapsed whole days rather than time-unanswered: it is populated even where we did answer, so it describes time-unanswered only when `awaitingOurReply` is also true. `looksLikeBroadcast`: the prospect's most recent message reached another profile in this campaign word for word, which indicates a mass DM rather than an answer. All three are floors rather than verdicts - a blast only one lead received is indistinguishable from a real reply, and two people who send the same long template are both flagged. Top-level `awaitingOurReplyCount` spans the whole campaign rather than this page, and leaves out broadcasts and replies scored negative; it can exceed `returned` when `limit` is small.
    list_next_stepsReturns 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 only replies whose sentiment was scored, so it is a floor; a withdrawn or unreachable invite leaves no trace, so some 'never contacted' leads may already have been tried. Sentiment is judged once per lead, not per message. Counts are complete; leadIds are capped at 200.
    get_campaign_statsReturns the outreach funnel for one campaign: invites sent, invites accepted, messages sent, and replies split into positive / negative / other by sentiment. LinkedIn and email are merged, as on Onsa's Overview page. These count leads rather than actions - a lead invited twice counts once - so where a lead was re-invited they read slightly lower than the Overview widget, which counts actions. `invitesSent` can exceed `leadsTotal` without anyone having been invited twice: the counters are already de-duplicated by lead, and the gap means a lead who was contacted has since been skipped or deleted, which action rows survive and leadsTotal does not count. Onsa stores no post likes or emoji reactions at all, so 'LinkedIn reactions' in this data means the replies people sent; list_replies returns their text, and this tool only counts them. `rates` carries acceptancePct, replyPct, positivePct and negativePct, each already computed over its own correct denominator; leadsTotal is not one of those denominators, since it counts every prospect in the cohort including those never contacted. A rate is null when its denominator is 0, meaning nothing was sent so no rate exists - which is different from the counts above being genuinely 0. All four rates count only replies Onsa has scored, so an unscored or still-in-window reply appears in none of them, and list_replies can legitimately show more replies than the rates imply. Two further properties of the data: the *Scheduled counts are everything queued regardless of date, and sentiment is evaluated once per lead ever rather than once per reply. A campaign whose invites were never sent reads as all zeros, which is 'not tried yet' rather than 'failed'.