agenticx-tool-creator

Agent Building

Guide for creating custom tools in AgenticX including function decorator tools, MCP tool integration, tool registries, and remote tool access. Use when the user wants to create tools for agents, integrate external APIs as tools, build MCP servers, or extend agent capabilities with custom functions.

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Prompt to paste

I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/DemonDamon/AgenticX/blob/HEAD/agenticx/skills/agenticx-tool-creator/SKILL.md

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AgenticX Tool Creator

Guide for building tools that extend agent capabilities.

Tool Architecture

AgenticX tools inherit from BaseTool and are consumed by agents during execution. Three approaches exist:

Function decorator (@tool) — fastest for simple tools
Class-based (extend BaseTool) — for complex or stateful tools
MCP remote tools — for external services via Model Context Protocol

Function Decorator Tools

from agenticx.tools import tool

@tool
def search_web(query: str) -> str:
    """Search the web for information.

    Args:
        query: The search query string.

    Returns:
        Search results as text.
    """
    # implementation
    return f"Results for: {query}"

@tool
def read_file(path: str) -> str:
    """Read contents of a local file."""
    with open(path) as f:
        return f.read()

The @tool decorator reads the function signature and docstring to generate the tool schema automatically. The docstring is the tool description the LLM sees.

Class-Based Tools

For tools needing initialization, state, or complex logic:

from agenticx.core import BaseTool

class DatabaseQuery(BaseTool):
    name = "database_query"
    description = "Query the project database."

    def __init__(self, connection_string: str):
        super().__init__()
        self.conn = connect(connection_string)

    def _run(self, sql: str) -> str:
        return self.conn.execute(sql).fetchall()

Tool Registry

from agenticx.core import ToolRegistry

registry = ToolRegistry()
registry.register(search_web)
registry.register(read_file)

# List all registered tools
for t in registry.list_tools():
    print(f"{t.name}: {t.description}")

MCP Integration

AgenticX supports the Model Context Protocol for remote tool access.

Connecting to an MCP Server

from agenticx.protocols import MCPClient

client = MCPClient(server_url="http://localhost:3000")
tools = client.list_tools()

# Use MCP tools like local tools
result = client.call_tool("search", {"query": "AI agents"})

Building an MCP Server

AgenticX agents can be exposed as MCP-compatible services:

from agenticx.protocols import MCPServer

server = MCPServer(host="0.0.0.0", port=3000)
server.register_tool(search_web)
server.register_tool(read_file)
server.start()

Skill-Based Tools

Skills (SKILL.md bundles) are also exposed as tools via SkillTool:

from agenticx.tools.skill_bundle import SkillBundleLoader, SkillTool

loader = SkillBundleLoader()
skill_tool = SkillTool(loader=loader)
# Agents can invoke: skill_tool("list") or skill_tool("read <skill-name>")

Tool Design Guidelines

Clear docstrings — the LLM uses the docstring to decide when to call the tool
Type hints — always annotate parameters and return types
Error handling — return descriptive error messages, don't raise bare exceptions
Minimal scope — one tool, one purpose
Idempotent when possible — safe to retry without side effects
Test independently — verify tools work before attaching to agents

Advanced: Tool Context

Tools can access execution context:

@tool
def contextual_tool(query: str, _context: "ToolContext" = None) -> str:
    """A tool that uses execution context."""
    if _context:
        user = _context.user
        session = _context.session_id
    return f"Processed: {query}"