Tools

Connect Deep Agents to custom functions, APIs, databases, and any MCP server

Deep Agents can call any tool you define, any LangChain tool, and tools from any MCP server. Pass them to create_deep_agent via the tools= parameter alongside the built-in harness tools for planning, file management, and subagent spawning.

Google

from deepagents import create_deep_agent


agent = create_deep_agent(
    model="google_genai:gemini-3.5-flash",
    tools=[search, fetch_url, run_query],
)

Custom tools

Pass any callable, such as plain functions, LangChain @tool-decorated functions, or tool dicts—directly to tools=. Deep Agents infers the tool schema from the function signature and docstring, so you don’t need to define a separate schema in most cases.

Google

import os
from typing import Literal
from tavily import TavilyClient
from deepagents import create_deep_agent

tavily_client = TavilyClient(api_key=os.environ["TAVILY_API_KEY"])


def internet_search(
    query: str,
    max_results: int = 5,
    topic: Literal["general", "news", "finance"] = "general",
    include_raw_content: bool = False,
):
    """Run a web search"""
    return tavily_client.search(
        query,
        max_results=max_results,
        include_raw_content=include_raw_content,
        topic=topic,
    )


agent = create_deep_agent(
    model="google_genai:gemini-3.5-flash",
    tools=[internet_search],
)

For full details on defining and using LangChain tools (tool dicts, StructuredTool, return types, error handling, and more), see Tools.

MCP tools

Deep Agents fully support Model Context Protocol (MCP), the open standard for connecting agents to external services. Load tools from any MCP server and pass them directly to create_deep_agent.

MCP is an open protocol that lets agents connect to a growing ecosystem of servers—databases, APIs, file systems, browsers, and more—through a standard interface. Instead of writing custom integration code for each service, you point Deep Agents at an MCP server and it gets all the tools that server exposes.

Install langchain-mcp-adapters to connect to MCP servers:

pip install langchain-mcp-adapters

Google

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from deepagents import create_deep_agent


async def main():
    client = MultiServerMCPClient(
        {
            "my_server": {
                "transport": "http",
                "url": "http://localhost:8000/mcp",
            }
        }
    )
    tools = await client.get_tools()

    agent = create_deep_agent(
        model="google_genai:gemini-3.5-flash",
        tools=tools,
    )

    result = await agent.ainvoke(
        {"messages": [{"role": "user", "content": "Use the MCP server to help me."}]},
        config={"configurable": {"thread_id": "1"}},
    )


asyncio.run(main())

For detailed configuration options — including stdio servers, OAuth authentication, tool filtering, and stateful sessions — see the full MCP guide.

Built-in harness tools

In addition to the tools you provide, every Deep Agent comes with a built-in set of tools from the harness:

Tool	Description
ls	List files in a directory
read_file	Read file contents (with pagination and multimodal support)
write_file	Create a new file, or overwrite an existing one
edit_file	Perform exact string replacements in files
glob	Find files matching a glob pattern
grep	Search file contents
execute	Run shell commands (sandbox backends only)
task	Spawn a subagent to handle a delegated task
write_todos	Manage a structured todo list

write_file overwrites a file if it already exists, as of deepagents>=0.7.0a2. On earlier versions, write_file errors instead. Use edit_file to modify an existing file.

For a full breakdown of what each built-in tool does, see Harness overview.

Multimodal tool outputs

Custom tools can return plain text or standard content blocks (text, images, audio, video, and files) when the selected model supports multimodal tool results. The built-in read_file tool also returns multimodal blocks for supported non-text file types.

Return a string for text-only results, or an ordered list of content blocks for text plus media or interleaved multimodal output. See Multimodal and Tool return values for examples and context-compression considerations.

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