Botdog

@botdog list all active campaigns owned by Sarah and show me their invitation acceptance rates

Found 4 active campaigns owned by Sarah: 1. **Q1 Enterprise Outreach** – 847 invitations sent, 23.4% acceptance rate 2. **Product Launch Follow-up** – 312 invitations sent, 31.8% acceptance rate 3. **Partner Recruitment EMEA** – 1,204 invitations sent, 18.9% acceptance rate 4. **Reactivation Campaign Dec** – 556 invitations sent, 12.1% acceptance rate The Product Launch campaign is performing significantly above team average (31.8% vs. 21% baseline). Enterprise Outreach and Partner Recruitment are tracking close to expected rates for cold outreach in those segments.

This example combines list-campaigns and get-campaign-statistics calls to surface performance data. Requires read access to team analytics. Acceptance rates depend on LinkedIn's delivery and the quality of your targeting; Botdog reports what LinkedIn confirms, but cannot guarantee visibility into shadow-banning or throttling.

@botdog add these 15 LinkedIn profile URLs to the 'Q2 SaaS Prospects' campaign and confirm they were queued

Successfully added 15 leads to campaign **Q2 SaaS Prospects** (ID: cmp_8x3k9). All profiles passed validation and are now queued in the campaign's workflow. **Important:** This campaign has an active sequence starting with a connection request within 2 hours. The new leads will enter that workflow immediately. If you need to adjust timing or messaging, pause the campaign first or edit the sequence before adding leads. Campaign owner: Michael Torres | Current lead count: 1,547 → 1,562

Demonstrates the add-leads-to-campaign tool, which writes data and triggers side-effects. The MCP will not validate profile URLs beyond basic format checks—invalid or private profiles may fail silently in Botdog's queue. Always confirm the campaign's sequence settings before bulk-adding leads to avoid unintended outreach.

@botdog pull daily analytics for the last 30 days and identify which days had the highest reply rates, then explain any patterns you see

Analyzed 30 days of outreach activity (Dec 15–Jan 13). Top 5 days by reply rate: - **Dec 19 (Thu):** 8.2% reply rate, 340 messages sent - **Jan 8 (Wed):** 7.9% reply rate, 412 messages sent - **Dec 27 (Fri):** 7.4% reply rate, 189 messages sent - **Jan 10 (Fri):** 7.1% reply rate, 298 messages sent - **Dec 21 (Sat):** 6.8% reply rate, 104 messages sent **Pattern:** Mid-week (Wed–Thu) consistently outperforms Monday and Tuesday. The Dec 27 spike is notable—holiday week, lower volume, but higher engagement suggests less inbox competition. Saturdays show decent rates but very low send volume, indicating selective manual outreach rather than automated sequences.

This synthesis example pairs get-daily-analytics with the AI's reasoning to surface actionable insights. Botdog's analytics reflect LinkedIn's reported events; reply detection depends on LinkedIn's API and may miss replies sent outside the platform or after a lead disconnects. Use this for trend-spotting, not compliance reporting.

Rolling up outreach metrics when your reps work in different time zones

A sales ops lead at a 20-person company with reps in New York, Austin, and Berlin needs to build a monthly LinkedIn outreach report for the VP of Sales. She uses Get Analytics with the per-user breakdown to pull invitation counts, message volume, and response rates for each rep across the last 30 days, then drops the numbers into a Google Sheet. The MCP handles the time-zone math and returns totals in UTC, so she doesn't have to reconcile three different Botdog exports. This works when your reporting cadence is weekly or monthly and you're okay with summary stats—invitations sent, messages delivered, replies received. It breaks down if you need to audit individual lead interactions or trace a specific conversation thread back to a LinkedIn profile. For distributed teams that measure outbound activity in aggregate and don't need forensic-level detail, this MCP turns a two-hour manual export into a five-minute API call.