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create_campaign

Destructive

Build a LinkedIn outreach campaign from a natural language description. Finds matching people and saves a draft; nothing sends until you launch it.

Instructions

Create a LinkedIn outreach campaign from a natural language description.

Finds people on LinkedIn who match the description and saves a draft;
nothing is sent until campaign(action='launch'). Use it for sales
prospecting, recruiting and candidate sourcing, research and
user-interview recruitment, job-search networking, investor and partner
outreach, vendor scouting and event invitations.

Reaching people you can already name — an article author, a warm intro, a
speaker, one founder a customer mentioned — is the `people` argument: pass
their profile URLs and the campaign is seeded from exactly them, with no
LinkedIn search. Everything else is unchanged: draft until launch, rate
limits, sending window, opt-outs.

Describe who you need to reach and HeyLead finds them on LinkedIn:
customers (lead generation, B2B prospecting, cold outreach), candidates
(recruiting and sourcing), hiring managers and referrals (job search),
investors and partners, vendors, and user-interview or research participants.
On first campaign, project_brief is asked explicitly, in the goal's words
(see project_brief below) — a homepage alone is not enough.

Args:
    target_description: Who to target (e.g., "CTOs at fintech startups",
        "freelance UX designers in London", "yoga studio owners in California")
    campaign_name: Optional name for the campaign.
    icp_id: Optional ID of a saved ICP from generate_icp. If provided,
        uses the saved ICP's enriched LinkedIn codes for precise targeting
        instead of generating a new one.
    company_context: Optional. Your website URL or 1-2 sentences about your
        product/company. Copied into project_brief when project_brief is omitted.
    project_brief: Optional. Full project paste the model sees, in the goal's
        words. sell: what you offer and who it is for. job_search: the role,
        the kind of company, what you bring. hire: the role, who fits it, what
        it offers. partner: what you want from a partner, what you bring.
        buy: what you need, by when and how much, what a vendor must confirm.
        research: what you research, who you want to hear from, what you ask.
        Required before launch, resume, or auto-send.
    mode: Always autopilot (copilot mode was removed). Whether opening DMs
        and follow-ups wait for a person is the WORKSPACE's approval mode,
        not this: a hosted workspace that never chose holds them
        (inspect(action="waiting") lists them); scheduler(action=
        "approval_mode") switches it.
    company_url: Optional LinkedIn company URL for account-based targeting.
        Searches for employees at that specific company matching the ICP.
        Example: "https://www.linkedin.com/company/google"
    voice_mode: "text_only". Messages are text.
    connections_only: "on" to create a DM-only campaign targeting existing
        LinkedIn connections. Skips invitations and warm-up — sends DMs
        directly to people you're already connected with. Use when user says
        "existing connections", "DM my network", "message my connections".
    exclude_connections: ON BY DEFAULT for a new campaign: nobody who was
        already a 1st-degree connection before this campaign started is
        reached — refused at enrolment and skipped at send time rather than
        DMed. People who accept this campaign's own invitation still get
        the opener. Pass "off" to include existing connections (or turn it
        off later in the campaign settings). Defaults off only for a
        connections_only campaign. Use "off" when the user says
        "include my existing connections"; the old "on" is still accepted
        for "don't message my existing connections", "cold only",
        "skip people I already know". Cannot be combined with
        connections_only, which is its exact inverse.
    people: LinkedIn profile URLs or public identifiers, comma or newline
        separated (e.g. "linkedin.com/in/jane-doe, linkedin.com/in/john-doe").
        The campaign is seeded from exactly these people: no LinkedIn
        search runs, the goal <-> ICP audit is skipped (the audience is
        stated, not inferred), a low ICP score does not drop anybody, and
        discovery stays off so nothing tops the queue up with strangers.
        Use it whenever the user names who to reach. Cannot be combined
        with connections_only.
    campaign_type: Prompt family: "outbound" (default) or "job_search".
        job_search writes a job-search campaign: the invitation note and
        the first DM may name the recipient's company and the role, use
        one credible proof point at most and never list a CV. InMail is
        not routed by this switch. Pair with connections_only="on" to
        write to people the sender is already connected to.
    force: True to create the campaign even when the goal <-> ICP audit
        returns `mismatch` (the ICP holds no plausible buyer for the goal).
        Leave False; a `partial` verdict never blocks, it only warns.
    goal: What the campaign is for: "sell" (default), "job_search",
        "hire", "partner", "buy" or "research". It sets campaign_type and
        campaign_intent, picks whose profile the ICP describes, and picks
        the fit question the goal <-> ICP audit asks. hire, partner and
        research run on your project_brief until their message sets exist.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
goalNo
modeNoautopilot
forceNo
icp_idNo
peopleNo
voice_modeNotext_only
company_urlNo
campaign_nameNo
campaign_typeNo
project_briefNo
company_contextNo
connections_onlyNo
target_descriptionYes
exclude_connectionsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.10.398
    • addedInput schema / properties / goal
      Added value: +{
      +  "default": "",
      +  "title": "Goal",
      +  "type": "string"
      +}
  2. Changed1 schema field changedv0.10.389
    • addedInput schema / properties / people
      Added value: +{
      +  "default": "",
      +  "title": "People",
      +  "type": "string"
      +}
  3. First observedv0.10.375

TDQS

A4.6/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations declare destructiveHint=true and openWorldHint=true, but the description goes further by clarifying that nothing is sent until launch, that rate limits, sending window and opt-outs still apply, that project_brief is required before launch/resume/auto-send, and that approval behavior is a workspace setting surfaced via inspect/scheduler. It does not explicitly reconcile the destructiveHint against the fact that only a draft is created, which is the one gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the purpose and the draft-until-launch rule, and arg docs are alphabetical and readable. However the use-case list is stated twice nearly verbatim (sales prospecting/recruiting/research in the opening paragraphs, then again as 'customers... candidates... investors...'), which is avoidable bloat in an already long description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present, the description needn't explain return values, and everything else an agent needs for a 14-param, open-world, mutating tool — prerequisites, parameter interactions, defaults, and the goal taxonomy — is present.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% across 14 parameters, so the description carries the full burden — and it does, documenting every parameter with defaults, conflicts (exclude_connections cannot combine with connections_only), and value enums for goal/campaign_type/voice_mode. Examples are supplied for target_description, company_url and people.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Create a LinkedIn outreach campaign') plus the input modality ('from a natural language description') and immediately scopes it as a draft until campaign(action='launch'). An agent can distinguish this from campaign, edit_campaign, and generate_icp without opening a schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly routes the agent: use the `people` argument when the user can already name targets (skips LinkedIn search), use `connections_only` for 'DM my network', use `exclude_connections='off'` when the user says 'include my existing connections', and use `force` only on a mismatch verdict. Alternatives and when-not conditions are named rather than implied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.