hermes-agent/docs/tools.md

Tools

Tools are functions that extend the agent's capabilities. Each tool is defined with an OpenAI-compatible JSON schema and an async handler function.

Tool Structure

Each tool module in tools/ exports:

1. Schema definitions - OpenAI function-calling format
2. Handler functions - Async functions that execute the tool

# Example: tools/web_tools.py

# Schema definition
WEB_SEARCH_SCHEMA = {
    "type": "function",
    "function": {
        "name": "web_search",
        "description": "Search the web for information",
        "parameters": {
            "type": "object",
            "properties": {
                "query": {"type": "string", "description": "Search query"}
            },
            "required": ["query"]
        }
    }
}

# Handler function
async def web_search(query: str) -> dict:
    """Execute web search and return results."""
    # Implementation...
    return {"results": [...]}

Tool Categories

Category	Module	Tools
Web	web_tools.py	web_search, web_extract, web_crawl
Terminal	terminal_tool.py	terminal (local/docker/singularity/modal/ssh backends)
File	file_tools.py	read_file, write_file, patch, search
Browser	browser_tool.py	browser_navigate, browser_click, browser_type, etc.
Vision	vision_tools.py	vision_analyze
Image Gen	image_generation_tool.py	image_generate
TTS	tts_tool.py	text_to_speech (Edge TTS free / ElevenLabs / OpenAI)
Reasoning	mixture_of_agents_tool.py	mixture_of_agents
Skills	skills_tool.py	skills_list, skill_view
Todo	todo_tool.py	todo (read/write task list for multi-step planning)
Cronjob	cronjob_tools.py	schedule_cronjob, list_cronjobs, remove_cronjob
RL Training	rl_training_tool.py	rl_list_environments, rl_start_training, rl_check_status, etc.

Tool Registration

Tools are registered in model_tools.py:

# model_tools.py
TOOL_SCHEMAS = [
    *WEB_TOOL_SCHEMAS,
    *TERMINAL_TOOL_SCHEMAS,
    *BROWSER_TOOL_SCHEMAS,
    # ...
]

TOOL_HANDLERS = {
    "web_search": web_search,
    "terminal": terminal_tool,
    "browser_navigate": browser_navigate,
    # ...
}

Toolsets

Tools are grouped into toolsets for logical organization (see toolsets.py):

TOOLSETS = {
    "web": {
        "description": "Web search and content extraction",
        "tools": ["web_search", "web_extract", "web_crawl"]
    },
    "terminal": {
        "description": "Command execution",
        "tools": ["terminal", "process"]
    },
    "todo": {
        "description": "Task planning and tracking for multi-step work",
        "tools": ["todo"]
    },
    # ...
}

Adding a New Tool

1. Create handler function in tools/your_tool.py
2. Define JSON schema following OpenAI format
3. Register in model_tools.py (schemas and handlers)
4. Add to appropriate toolset in toolsets.py
5. Update tools/__init__.py exports

Stateful Tools

Some tools maintain state across calls within a session:

• Terminal: Keeps container/sandbox running between commands
• Browser: Maintains browser session for multi-step navigation

State is managed per task_id and cleaned up automatically.

Terminal Backends

The terminal tool supports multiple execution backends:

Backend	Description	Use Case
local	Direct execution on host	Development, simple tasks
ssh	Remote execution via SSH	Sandboxing (agent can't modify its own code)
docker	Docker container	Isolation, reproducibility
singularity	Singularity/Apptainer	HPC clusters, rootless containers
modal	Modal cloud	Scalable cloud compute, GPUs

Configure via environment variables or cli-config.yaml:

# SSH backend example (in cli-config.yaml)
terminal:
  env_type: "ssh"
  ssh_host: "my-server.example.com"
  ssh_user: "myuser"
  ssh_key: "~/.ssh/id_rsa"
  cwd: "/home/myuser/project"

The SSH backend uses ControlMaster for connection persistence, making subsequent commands fast.

Skills Tools (Progressive Disclosure)

Skills are on-demand knowledge documents. They use progressive disclosure to minimize tokens:

Level 0: skills_categories()     → ["mlops", "devops"]           (~50 tokens)
Level 1: skills_list(category)   → [{name, description}, ...]   (~3k tokens)
Level 2: skill_view(name)        → Full content + metadata       (varies)
Level 3: skill_view(name, path)  → Specific reference file       (varies)

Skill directory structure:

skills/
└── mlops/
    └── axolotl/
        ├── SKILL.md           # Main instructions (required)
        ├── references/        # Additional docs
        ├── templates/         # Output formats, configs
        └── assets/            # Supplementary files (agentskills.io)

SKILL.md uses YAML frontmatter (agentskills.io compatible):

---
name: axolotl
description: Fine-tuning LLMs with Axolotl
metadata:
  hermes:
    tags: [Fine-Tuning, LoRA, DPO]
---

Skills Hub

The Skills Hub enables searching, installing, and managing skills from online registries. It is user-driven only — the model cannot search for or install skills.

Sources: GitHub repos (openai/skills, anthropics/skills, custom taps), ClawHub, Claude Code marketplaces, LobeHub.

Security: Every downloaded skill is scanned by tools/skills_guard.py (regex patterns + optional LLM audit) before installation. Trust levels: builtin (ships with Hermes), trusted (openai/skills, anthropics/skills), community (everything else — any findings = blocked unless --force).

Architecture:

• tools/skills_guard.py — Static scanner + LLM audit, trust-aware install policy
• tools/skills_hub.py — SkillSource ABC, GitHubAuth (PAT + App), 4 source adapters, lock file, hub state
• hermes_cli/skills_hub.py — Shared do_* functions, CLI subcommands, /skills slash command handler

CLI: hermes skills search|install|inspect|list|audit|uninstall|publish|snapshot|tap Slash: /skills search|install|inspect|list|audit|uninstall|publish|snapshot|tap