# ResearchMCP

> ResearchMCP — chew-z-researchmcp. Use this tool when you need to manage and deploy AI models, specifically Perplexity, on a server. ResearchMCP solves problems related to model serving, version control, and collaboration by providing a centralized platform with git integration. It takes in model configurations and code as input and outputs a deployed model server, ideal for use cases requiring scalable and reproducible AI deployments.

Canonical page: https://skillsregistry.net/skills/chew-z-researchmcp  
JSON: https://api.skillsregistry.net/v1/skills/chew-z-researchmcp

## Description

MCP server for Perplexity

## Trust

- **Trust score (0–1):** 0.95
- **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/chew-z/ResearchMCP)

## 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": "chew-z-researchmcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/chew-z-researchmcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/chew-z-researchmcp/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