brave-mcp-langchain
Enables Brave Search and web content fetching via MCP, and integrates as LangChain tools for AI agents.
README
brave-mcp-langchain
Create venv
uv sync
Install package
uv pip install brave-mcp-langchain
Run MCP server in STDIO mode
uvx brave-mcp-langchain
To run MCP server in SSE mode
uvx brave-mcp-langchain sse 5003
MCP Setting
{
"mcpServers": {
"brave-mcp-langchain": {
"disabled": false,
"timeout": 60,
"type": "stdio",
"command": "uvx",
"args": [
"brave-mcp-langchain"
]
}
}
}
Use as Langchain tool
It can also be used as Langchain tool. Below is how to validate tool.
import httpx
import asyncio
from langchain.tools import Tool
from brave_mcp_langchain import brave_tool
async def test_search():
result = await brave_tool.search_tool.ainvoke({"query": "LangGraph overview", "max_results": 10})
print(result)
result = await brave_tool.fetch_content_tool.ainvoke({
"url": "https://iamatulsingh.github.io"
})
print(result)
asyncio.run(test_search())
Use with langchain example
import asyncio
from langchain.agents import initialize_agent
from langchain.agents.agent_types import AgentType
from langchain_ollama import ChatOllama
from brave_mcp_langchain import brave_tool
llm = ChatOllama(model="llama3.1:8b")
tools = [
brave_tool.search_tool,
brave_tool.fetch_content_tool
]
agent = initialize_agent(
tools=[brave_tool.search_tool, brave_tool.fetch_content_tool],
llm=llm,
agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
verbose=True
)
async def run_agent_query():
response = await agent.ainvoke(
"Search for 'iamatulsingh' overview, then fetch content from https://iamatulsingh.github.io"
)
print("\nAgent Response:")
print(response)
asyncio.run(run_agent_query())
🧠Inspiration & Attribution
This project, brave-mcp-langchain, was inspired by and partially based on the excellent work in duckduckgo-mcp-server by @nickclyde. That project laid the groundwork for integrating DuckDuckGo search and content fetching into the MCP ecosystem.
While brave-mcp-langchain extends the concept to support Brave Search and LangChain workflows, several architectural ideas and implementation patterns were adapted from duckduckgo-mcp-server, which is licensed under the MIT License.
I'm grateful for the open-source community and contributors who make projects like this possible. If you’re interested in DuckDuckGo-based search tools, definitely check out the original repository!
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.