# @resonia/veilmail-mcp

> @resonia/veilmail-mcp — resonia-health-veilmail-mcp. Use this tool when you need to integrate email functionality into AI workflows, enabling tasks such as sending emails, managing templates and audiences, and retrieving analytics through natural language inputs. It solves problems related to automated email communication, template management, and audience segmentation, providing outputs such as sent email confirmations and analytics data. It is ideal for use cases where AI agents require seamless email integration, using natural language as the interface to interact with the Veil Mail API.

Canonical page: https://skillsregistry.net/skills/resonia-health-veilmail-mcp  
JSON: https://api.skillsregistry.net/v1/skills/resonia-health-veilmail-mcp

## Description

A Model Context Protocol (MCP) server that exposes Veil Mail API operations as tools for AI agents, enabling email sending, template management, audience management, and analytics retrieval through natural language.

## Trust

- **Trust score (0–1):** 0.70
- **Verification tier:** verified
- **Last scanned:** 2026-08-29

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** communication
- **Updated:** 2026-08-29

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/apuqbmkvgp)
- **Repository:** <https://github.com/Resonia-Health/veilmail-mcp>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "resonia-health-veilmail-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/resonia-health-veilmail-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/resonia-health-veilmail-mcp/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt