About anima
anima is a hosted MCP server that gives AI agents email, phone, SMS, voice, and credential-management capabilities. It exposes the Anima platform through one gateway and domain-specific endpoints.
Key features
- 01Manage agent identities and lifecycle
- 02Create and manage email inboxes
- 03Send mail and handle threads
- 04Provision numbers and manage SMS
- 05Handle voice calls, transcripts, and recordings
- 06Store credentials and manage OAuth connections
Use cases
- 01Set up a dedicated email address for an AI agent
- 02Handle communication tasks through SMS and voice
- 03Store and retrieve authentication credentials
MCP support
The hosted MCP server exposes its tools to clients over streamable HTTP with Bearer authorization. The /mcp endpoint provides the full tool set, alongside domain-specific endpoints.
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://mcp.useanima.sh/mcpclaude mcp add --transport http anima-labs-ai-anima https://mcp.useanima.sh/mcp{"mcpServers":{"anima-labs-ai-anima":{"type":"http","url":"https://mcp.useanima.sh/mcp"}}}Open in Cursor{"servers":{"anima-labs-ai-anima":{"type":"http","url":"https://mcp.useanima.sh/mcp"}}}Open in VS Code{"mcpServers":{"anima-labs-ai-anima":{"type":"http","url":"https://mcp.useanima.sh/mcp"}}}{"mcpServers":{"anima-labs-ai-anima":{"serverUrl":"https://mcp.useanima.sh/mcp"}}}Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.
Tools
Details
From the project README@anima-labs/mcp-server
The hosted MCP server behind https://mcp.useanima.sh — the recommended way to connect AI assistants to Anima. It exposes the full tool surface (66 tools today) over streamable HTTP with Authorization: Bearer <ak_… or mk_…>.
This package is deliberately not published to npm: it is the deployed gateway. For a local/stdio server use @anima-labs/mcp (published, 53 core tools) — same platform, same auth. Client setup for both lives in the MCP docs, or run anima setup-mcp.
Endpoints
One gateway, scoped mounts. /mcp carries everything; the per-domain mounts serve tailored connections with a smaller tool count:
/mcp— all tools (recommended)/agent— agent lifecycle (create/get/list/update/delete)/email— Anima agent mailboxes: inbox create/get/list/update/delete, send/reply/forward, threads, drafts, attachments, custom domains (agents.useanima.shaddresses or your own domain — no Gmail/SMTP relay involved)/phone— phone number provisioning, SMS threads, voice calls + transcripts/recordings (Telnyx-backed)/platform— account/usage overviews and webhook management/vault— encrypted credential vault: CRUD, search, TOTP, server-side use, credential requests, OAuth connections, Connect Links/extension— browser extension connect (headless / Puppeteer)
Run locally (development)
bun run src/index.ts
Environment: ANIMA_API_URL (defaults to a local dev API at http://127.0.0.1:3100; the production deployment sets it to the live API), MCP_BASE_URL, PORT.
Deploy production (mcp.useanima.sh)
Preferred path is GitHub Actions: run Deploy MCP Server (.github/workflows/deploy.yml) via workflow_dispatch.
Required repository secrets (for Workload Identity Federation / OIDC):
GCP_WORKLOAD_IDENTITY_PROVIDER(format:projects/<number>/locations/global/workloadIdentityPools/<pool>/providers/<provider>)GCP_SERVICE_ACCOUNT_EMAIL(deployer service account inanima-labs)
Workflow behavior:
- Builds and pushes
us-central1-docker.pkg.dev/anima-labs/anima/mcp-server:<tag> - Deploys Cloud Run service
mcp-serverinus-central1 - Prints
GET /healthresponse so you can confirmstartedAtchanged
Manual fallback (same image/deploy settings) remains:
./deploy.sh <tag>
History
Replaces five separate packages (mcp-agent, mcp-email, mcp-phone, mcp-platform, mcp-vault) and the mcp-core shared library.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.