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toolfs — how to use toolfs how to use toolfs, what is toolfs, toolfs alternative, toolfs vs fuse, toolfs install, toolfs setup guide, toolfs for ai agents, toolfs plugin system, toolfs virtual filesystem

v1.0.0
GitHub

About this Skill

Ideal for LLM Agents requiring unified access to files, memory, RAG systems, skills, and snapshots through a single virtual filesystem namespace ToolFS is a FUSE virtual filesystem for AI agents, integrating memory, RAG, and local data access with flexible tool chaining and a scalable plugin system

Features

Integrates persistent key-value storage for session data and context via memory
Provides semantic search over vector databases for document retrieval via RAG
Offers access to mounted local directories through a unified virtual filesystem
Supports skills and snapshots through a single /toolfs namespace
Features a scalable plugin system for flexible MCP/tool chaining

# Core Topics

IceWhaleTech IceWhaleTech
[0]
[0]
Updated: 3/7/2026

Quality Score

Top 5%
65
Excellent
Based on code quality & docs
Installation
SYS Universal Install (Auto-Detect)
Cursor IDE Windsurf IDE VS Code IDE
> npx killer-skills add IceWhaleTech/ToolFS/skill/snapshot/filesystem/rag/snapshot/SKILL.md

Agent Capability Analysis

The toolfs MCP Server by IceWhaleTech is an open-source Categories.community integration for Claude and other AI agents, enabling seamless task automation and capability expansion. Optimized for how to use toolfs, what is toolfs, toolfs alternative.

Ideal Agent Persona

Ideal for LLM Agents requiring unified access to files, memory, RAG systems, skills, and snapshots through a single virtual filesystem namespace

Core Value

Empowers agents to integrate multiple data sources and operations, providing access to persistent key-value storage, semantic search over vector databases, and mounted local directories through protocols like ToolFS, enhancing their capabilities with RAG systems and skills management

Capabilities Granted for toolfs MCP Server

Accessing session data and context through memory
Performing semantic search over vector databases for document retrieval
Mounting local directories for file access
Managing skills and snapshots through a unified interface

! Prerequisites & Limits

  • Requires ToolFS namespace setup
  • Limited to LLM agents
  • Dependent on underlying filesystem and RAG system infrastructure
Project
SKILL.md
5.0 KB
.cursorrules
1.2 KB
package.json
240 B
Ready
UTF-8

# Tags

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SKILL.md
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ToolFS

ToolFS is a unified virtual filesystem framework for LLM agents that provides access to files, memory, RAG systems, skills, and snapshots through a single /toolfs namespace.

Overview

ToolFS integrates multiple data sources and operations into one virtual filesystem:

  • Memory: Persistent key-value storage for session data and context
  • RAG: Semantic search over vector databases for document retrieval
  • Filesystem: Access to mounted local directories
  • Skills: Execute WASM-based skills mounted to virtual paths
  • Snapshots: Create point-in-time snapshots and restore previous states

All operations respect session isolation, permission control, and audit logging for safe execution in sandboxed environments.

Available Skills

ToolFS is organized into functional modules. Each module provides specific capabilities:

ModulePathDescriptionDocumentation
Memory/toolfs/memoryPersistent storage for session data and contextMemory Skill
RAG/toolfs/ragSemantic search over vector databasesRAG Skill
Filesystem/toolfs/<mount>Access to mounted local directoriesFilesystem Skill
Code/toolfs/<skill>Execute WASM or native skillsCode Skill
Snapshots/toolfs/snapshotsFilesystem state snapshots and rollbackSnapshot Skill

Quick Start

Memory Operations

bash
1# Read memory entry 2GET /toolfs/memory/<entry_id> 3 4# Write memory entry 5PUT /toolfs/memory/<entry_id> 6 7# List memory entries 8LIST /toolfs/memory

See Memory Skill for details.

RAG Search

bash
1# Semantic search 2GET /toolfs/rag/query?text=<query>&top_k=<number>

See RAG Skill for details.

Filesystem Access

bash
1# Read file 2GET /toolfs/<mount_point>/<relative_path> 3 4# Write file 5PUT /toolfs/<mount_point>/<relative_path> 6 7# List directory 8LIST /toolfs/<mount_point>/<relative_path>

See Filesystem Skill for details.

Skill Execution

bash
1# Execute skill 2GET /toolfs/<skill_mount_path>?text=<query>

See Skill Skill for details.

Snapshot Management

bash
1# Create snapshot 2POST /toolfs/snapshots/create 3 4# Rollback snapshot 5POST /toolfs/snapshots/rollback 6 7# List snapshots 8GET /toolfs/snapshots

See Snapshot Skill for details.

Skill API (Chained Operations)

Chain multiple operations in a single request:

json
1POST /toolfs/skills/chain 2Content-Type: application/json 3 4{ 5 "operations": [ 6 { 7 "type": "search_memory", 8 "query": "user preferences" 9 }, 10 { 11 "type": "search_rag", 12 "query": "ToolFS configuration", 13 "top_k": 5 14 }, 15 { 16 "type": "read_file", 17 "path": "/toolfs/data/config/settings.json" 18 } 19 ] 20}

Common Use Cases

  • File Operations: "Read the config file from the project directory"
  • Memory Persistence: "Store this conversation summary in memory"
  • Semantic Search: "Search documents for information about X"
  • Skill Execution: "Execute the RAG skill to find relevant content"
  • State Management: "Create a snapshot before making changes"
  • Recovery: "Restore the previous state"

Output Format

All operations return standardized result structures:

json
1{ 2 "type": "memory|rag|file|skill|snapshot", 3 "source": "identifier (ID, path, command, skill_name)", 4 "content": "string content or data", 5 "metadata": {}, 6 "success": true|false, 7 "error": "error message if failed" 8}

Error Handling

Errors are returned with structured responses:

json
1{ 2 "success": false, 3 "error": "Detailed error message", 4 "type": "error_type", 5 "source": "operation_identifier" 6}

Common error types:

  • access_denied: Session does not have permission
  • not_found: Resource not found
  • skill_error: Skill execution failed
  • validation_error: Invalid input parameters
  • filesystem_error: Filesystem operation failed

Best Practices

  1. Use Sessions: Always create sessions with appropriate allowed_paths for security
  2. Chain Operations: Use ChainOperations to minimize round trips
  3. Snapshot Before Changes: Create snapshots before major filesystem modifications
  4. Handle Errors: Check success field in results and provide fallback strategies
  5. Leverage Metadata: Use metadata fields to pass context between operations

Module Documentation

For detailed information about each module, see:


This documentation describes ToolFS version 1.0.0. Each module has its own detailed SKILL.md for specific operations.

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