Files
hermes-agent/docs/tools.md
teknium c360da4f35 Enhance documentation for CLI and tool integration
- Updated `.cursorrules` to provide a comprehensive overview of the interactive CLI, including its architecture, key components, and command handling.
- Expanded `README.md` to introduce the CLI features, quick start instructions, and detailed command descriptions for user guidance.
- Added `docs/cli.md` to document CLI usage, configuration, and animated feedback, ensuring clarity for users and developers.
- Revised `docs/tools.md` to include support for SSH backend in terminal tools, enhancing the documentation for terminal execution options.
2026-01-31 06:33:43 +00:00

4.4 KiB

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)
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
Reasoning mixture_of_agents_tool.py mixture_of_agents
Skills skills_tool.py skills_categories, skills_list, skill_view

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"]
    },
    # ...
}

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

SKILL.md uses YAML frontmatter:

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