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Otterly.ai

Otterly.ai MCP Server

Yes. Otterly.ai has an official MCP server, hosted at data.otterly.ai/mcp.

OFFICIAL MCP✗ unreachablePublic API
MCP status verified 2026-05-21Agent-readiness 7/10
Data from Buddy's database, auto-rechecked weekly

What is the Otterly.ai MCP server?

The Otterly.ai MCP server lets Claude, Cursor, Codex, and other MCP-compatible clients query core records, settings, and activity data without leaving the editor. The server is official, hosted by Otterly.ai themselves, so it should track product changes without lag.

Three things you actually do with it: ask Claude what changed in the last 7 days, have Cursor compare this period's metrics to last, and let Codex push a new record with custom metadata.

Otterly.ai also has a public REST API at docs.otterly.ai. Use the API for scheduled jobs and custom dashboards. Use the MCP server when an LLM is in the loop.

For the rest of the picture see the Otterly.ai review, full pricing breakdown, .

Inside Claude / Cursor / Codex

What you can build with the Otterly.ai MCP

Illustrative workflow examples. Exact tool names, parameters, and outputs come from Otterly.ai's MCP server schema — check the docs for the live surface.

otterly-ai-mcp
>Pull my latest Otterly.ai data into Claude
otterly-ai.get_latest()
LATEST ACTIVITY · 5
Most recent entry2 minutes ago
Records this week147
Active integrations4 connected
Storage used12.4% of plan
API calls today1,832
Drill into any of these?
otterly-ai-mcp
>Compare this month vs last month
otterly-ai.compare_periods({ period: "MoM" })
KEY METRICS · MoM
Total volume+18.4%
Active users+9.2%
Engagement rate−2.1%
Conversion rate+4.6%
Breakdown of the engagement dip?
otterly-ai-mcp
>What changed in the last 7 days that I should know about?
otterly-ai.recent_changes({ period: "7d" })
NOTABLE CHANGES · 3
New integration enabled2d ago
Plan limit reached92%
Top performer shifttoday
TMB SCORE FOR Otterly.aiMCP: OfficialAgent fit: 7/10
Read review →

How to connect Otterly.ai MCP to Claude Desktop

Add this block to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows), then restart Claude Desktop. Cursor and Codex use a similar schema.

{
  "mcpServers": {
    "otterly-ai": {
      "command": "npx",
      "args": ["mcp-remote", "<YOUR_MCP_URL>"]
    }
  }
}
// HTTP-transport MCP. Get your <YOUR_MCP_URL> from the vendor dashboard.
// Server source: https://data.otterly.ai/mcp
// Claude Code (one-liner): claude mcp add otterly-ai <YOUR_MCP_URL> --transport http

Some MCP servers require an API key. Check the source page for setup: data.otterly.ai/mcp.

Does Otterly.ai MCP cost extra?

Included with subscription

Otterly.ai ships an official MCP server that uses your existing subscription credentials.

  • No separate MCP licence to buy. You pay for your Otterly.ai plan, the MCP server queries that account.
  • Whether it's gated to a specific tier wasn't extracted automatically — verify the access requirements on the MCP page or pricing docs.

Otterly.ai also publishes a REST API

Otterly.ai publishes an API, but per-tier gating wasn't extracted automatically.

Auth method
SDK languages
OpenAPI spec
Webhooks
Structured outputs
See Otterly.ai API page

What you can do with Otterly.ai from an AI agent

Three shapes of agent workflow. Pick the row that matches how you actually use Claude / Cursor / Codex day to day.

INDIE / SOLO

Skip the dashboard, ask Claude

  • "What changed this week?" answered in seconds
  • Quick one-off questions without leaving the editor
  • Pipe Otterly.ai data into your personal notes / docs without writing a script
Effort: 30 seconds — paste the config, restart Claude
AGENCY

Run client analysis at agent speed

  • Hand the MCP server to your team — every consultant can ask Claude about any client's Otterly.ai account
  • Multi-account workflows without context-switching the Otterly.ai UI
  • Reports you'd normally spend hours on, done before standup
Effort: An afternoon — set up auth per client
IN-HOUSE TEAM

Production agent workflows

  • Embed in your internal Codex / Cursor agents that triage Otterly.ai data daily
  • Combine with other MCP servers in a multi-step agent chain
  • Constrain agents to read-only roles to keep production Otterly.ai safe
Effort: Days — security review + role config

Limits and gotchas

  • Official MCP server — published by Otterly.ai themselves, so it should track product changes.
  • ⚠ Server unreachable on last check. May be down, moved, or auth-gated.
  • Fallback: the REST API works for any flow the MCP server doesn't cover.
  • Rate limits: MCP calls count against your underlying Otterly.ai API quota. Burst usage from a curious agent can drain a daily allowance fast.
7/10

Agent-readiness verdict

Strong agent fit at a budget price. Otterly runs a live official MCP server at data.otterly.ai/mcp, authenticated with OAuth 2.0 (no API keys), exposing roughly 20 tools across workspaces, brand reports, GEO audits, and write actions, so agents can both read and act on your AI-visibility data. It pairs that with a public REST API (2,000 requests a month on Standard, 5,000 on Premium), an official Claude Skill, and a 101-workflow marketplace. The main gaps: the $29 Lite tier has no API or MCP, and there are no language SDKs, so it sits level with Peec and below Profound's broader enterprise stack.

Public API with docs

Scored 2026-06-07 by Joonas (TMB).

Otterly.ai MCP & API FAQ

Is the Otterly.ai MCP server official?+
Yes — published on Otterly.ai's own domain at https://data.otterly.ai/mcp.
Can I use the Otterly.ai MCP with Cursor or Codex?+
Yes — MCP is a protocol, not Claude-specific. Any MCP-capable client (Cursor, Codex, Windsurf, Zed) can use the same server config with minor path adjustments.
Does Otterly.ai have a public API?+
Yes — public REST API at docs.otterly.ai.

Sources

Data verified by Joonas on the dates shown. MCP server status auto-rechecked weekly.

Joonas Rotko
Joonas RotkoAuthor & Founder of That Marketing Buddy

10+ years in digital marketing. I review marketing software for AI-stack fit: real pricing, MCP and API support, and how cleanly each tool drops into an AI agent workflow, cross-checked against verified data and real user feedback.

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