# File Finder

> Use this tool when you need to locate specific files within a filesystem or retrieve detailed file metadata. It solves problems related to file discovery and organization by searching for files based on a path fragment and returning information such as file name, size, and creation date. The File Finder tool accepts a path fragment as input and outputs a list of matching files with their corresponding metadata.

Canonical page: https://skillsregistry.net/skills/kyan9400-file-finder  
JSON: https://api.skillsregistry.net/v1/skills/kyan9400-file-finder

## Description

This MCP server implementation provides a file finding functionality within the filesystem. It offers a tool to search for files based on a path fragment, returning detailed information such as file name, full path, size, and creation date. The server is built using Python and leverages the os module for filesystem operations, making it suitable for use cases requiring file discovery and metadata retrieval in local or networked environments.

## Trust

- **Trust score (0–1):** 1.00
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** search
- **Updated:** 2026-09-28

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/kyan9400-file-finder)
- **Repository:** <https://github.com/kyan9400/file-finder-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": "kyan9400-file-finder"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/kyan9400-file-finder` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/kyan9400-file-finder/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