# mcp-research-server

> mcp-research-server — rubychi-mcp-research-server. Use this tool when you need to build and deploy rich-context AI applications, such as conversational interfaces or language models, and integrate them with version control systems like Git. It solves problems related to developing and managing complex AI workflows, providing a hands-on implementation for researchers and developers. The tool accepts code and model inputs, and outputs trained models and application deployments, ideal for use cases requiring collaborative AI development and iteration.

Canonical page: https://skillsregistry.net/skills/rubychi-mcp-research-server  
JSON: https://api.skillsregistry.net/v1/skills/rubychi-mcp-research-server

## Description

A hands-on implementation from the MCP: Build Rich-Context AI Apps with Anthropic.

## Trust

- **Trust score (0–1):** 0.98
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Updated:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/rubychi/mcp-research-server)

## 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": "rubychi-mcp-research-server"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/rubychi-mcp-research-server` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/rubychi-mcp-research-server/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