# Rendobar MCP Server

> Use this tool when you need to process media files without server management, as it enables AI agents to run serverless media processing and upload local files, solving problems like resource-intensive file processing and storage limitations. It accepts local media files as input and outputs processed files, streamlining media workflows. Ideal for use cases requiring efficient, scalable media processing and upload capabilities.

Canonical page: https://skillsregistry.net/skills/rendobar-mcp  
JSON: https://api.skillsregistry.net/v1/skills/rendobar-mcp

## Description

Official MCP server for Rendobar. Lets AI agents run serverless media processing and upload local files.

## Trust

- **Trust score (0–1):** 0.69
- **Verification tier:** scanned
- **Last scanned:** 2026-09-03

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** cloud-infra
- **Updated:** 2026-09-03

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/kwdj3f0u3z)
- **Repository:** <https://github.com/rendobar/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": "rendobar-mcp"
    }
  }
}
```

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