# Filesystem

> Use this tool when you need to interact with file systems in a controlled and precise manner, solving problems such as data storage, retrieval, and manipulation. It provides a standardized interface for reading, writing, and managing files and directories, with detailed error handling and comprehensive tool descriptions. Ideal for use cases requiring robust file system operations, such as data processing, archiving, and synchronization.

Canonical page: https://skillsregistry.net/skills/quantgeekdev-filesystem  
JSON: https://api.skillsregistry.net/v1/skills/quantgeekdev-filesystem

## Description

Filesystem MCP server providing a robust set of file system interaction tools for AI agents. Implements core file operations like reading, writing, creating directories, moving files, and retrieving file metadata through a standardized TypeScript-based interface. Designed to enable precise, controlled file system manipulation with detailed error handling and comprehensive tool descriptions.

## 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:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-09-28

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/quantgeekdev-filesystem)
- **Repository:** <https://github.com/quantgeekdev/mcp-filesystem>

## 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": "quantgeekdev-filesystem"
    }
  }
}
```

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