# MCP Filesystem Server

> Use this tool when you need to analyze and visualize complex code repositories, evaluate directory structures, and generate call graphs for advanced code understanding. It solves problems related to code pattern detection, repository complexity evaluation, and semantic context building, providing optimized outputs for large language models. Ideal for use cases requiring efficient code analysis and visualization, with inputs including code repositories and outputs featuring visualized directory structures and call graphs.

Canonical page: https://skillsregistry.net/skills/williamrr-mcp-filesystem-rig  
JSON: https://api.skillsregistry.net/v1/skills/williamrr-mcp-filesystem-rig

## Description

Provides LLM-optimized tools for advanced code analysis, repository complexity evaluation, and call graph generation. It enables users to visualize directory structures, detect code patterns, and build semantic context with significant token savings.

## Trust

- **Trust score (0–1):** 0.90
- **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:** [Glama](https://glama.ai/mcp/servers/p1khmllpp0)
- **Repository:** <https://github.com/williamRR/mcp-filesystem-rig>

## 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": "williamrr-mcp-filesystem-rig"
    }
  }
}
```

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