Skip to content

Railtracks Tools

Extend Your Agents' Capabilities

Tools transform LLMs from chatbots into true agents that can take action in the world.

Railtracks provides a comprehensive suite of tools that extend the capabilities of your agents, allowing them to interact with external systems and services. These tools are the "hands and eyes" of your agents, enabling them to perform actions beyond just generating text.

Ways to Get Tools

Railtracks offers multiple ways to access and create tools:

  1. Built-in Tools - Use our pre-built tools for common tasks
  2. MCP Tools - Connect to Model Context Protocol servers for additional capabilities
  3. Create Your Own - Build custom tools for your specific needs

For a conceptual overview of tools in Railtracks, see the Tools Guide.

Available Tools

Built-in Tools

Tool Description Use Case
Python Execution Write and run Python code Data analysis, calculations, algorithmic tasks
Local Shell Execute commands in your local environment File operations, system management, running scripts

MCP-Based Tools

Tool Description Use Case
GitHub Interact with GitHub repositories Code management, issue tracking, PR reviews
Notion Create and manage Notion pages Knowledge management, documentation, project planning
Slack Send and receive Slack messages Team communication, notifications, updates
Web Search Search the web and retrieve information Research, fact-checking, data gathering

Getting Started

To use tools in your Railtracks agents, you'll typically follow these steps:

  1. Import the tools you want to use
  2. Create an agent that can access these tools
  3. Run the agent with your desired input
import railtracks as rt
from railtracks.nodes.library import connect_mcp
from railtracks.rt_mcp import MCPHttpParams

# Get tools from an MCP server
server = connect_mcp(MCPHttpParams(url="https://remote.mcpservers.org/fetch/mcp"))
tools = server.tools

# Create an agent with access to these tools
Agent = rt.agent_node(
    tool_nodes=tools,
    name="Research Agent",
    system_message="Use the tools to find information.",
    llm=rt.llm.OpenAILLM("gpt-4o"),
)

# Run the agent
with rt.Session():
    result = await rt.call(
        Agent,
        "Find information about Railtracks"
    )

Next Steps