# DingTalk

> Use this tool when you need to deploy and scale MCP services with ease, leveraging a containerized solution for consistent execution across environments. It solves problems related to server setup and configuration, enabling use cases such as AI-powered chatbots and automated data processing pipelines. With a Python-based interface and environment variable integration, it provides a robust framework for executing MCP commands and integrating with external services.

Canonical page: https://skillsregistry.net/skills/wllcnm-dingding  
JSON: https://api.skillsregistry.net/v1/skills/wllcnm-dingding

## Description

This MCP implementation, developed for DingDing, provides a Docker-based server for executing MCP commands. Built with Python, it leverages the MCP library and integrates with external services through environment variables. The implementation stands out by offering a containerized solution, ensuring consistent execution across different environments. By abstracting the complexities of server setup and configuration, it enables easy deployment and scaling of MCP services. This tool is particularly valuable for projects requiring robust MCP server capabilities, facilitating use cases such as AI-powered chatbots, automated data processing pipelines, and scalable machine learning applications.

## Trust

- **Trust score (0–1):** 0.63
- **Verification tier:** scanned
- **Last scanned:** 2026-09-01

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** cloud-infra
- **Updated:** 2026-09-01

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/wllcnm-dingding)
- **Repository:** <https://github.com/wllcnm/dingding-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": "wllcnm-dingding"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/wllcnm-dingding` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/wllcnm-dingding/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