mcp-server-deerflow-kinthai
OfficialClick on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@mcp-server-deerflow-kinthaideep research on quantum computing advancements"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
mcp-server-deerflow-kinthai
MCP Server that exposes DeerFlow deep capabilities via standard Model Context Protocol.
Any MCP client (OpenClaw, Claude Desktop, Cursor, etc.) can discover and invoke DeerFlow skills through this server.
Architecture
MCP Client (OpenClaw / Claude Desktop / Cursor)
|
| MCP protocol (SSE on :8808)
v
mcp-server-deerflow-kinthai
|
| LangGraph REST API (:2024)
v
DeerFlow (bytedance/deer-flow)
|
+-- deep research (multi-source web search + cross-verification)
+-- data analysis (DuckDB)
+-- chart visualization (26+ chart types)
+-- PPT generation
+-- image generation
+-- consulting analysis (SWOT, Porter's, etc.)The server is a thin wrapper: it translates MCP tool calls into DeerFlow LangGraph runs, extracts the response text and artifacts, and returns them in MCP format. DeerFlow itself remains untouched upstream.
Tools
Tool | Description |
| Multi-source web research with cross-verification |
| Data analysis with DuckDB (CSV/Excel) |
| 26+ chart types (line, bar, pie, scatter, sankey, etc.) |
| PowerPoint presentation generation |
| AI image generation |
| Business analysis (SWOT, Porter's Five Forces, etc.) |
All tools accept a query string (required) and an optional agent_name for specialized DeerFlow agent personas.
Quick Start
# Install
pip install mcp-server-deerflow-kinthai
# Run (requires a running DeerFlow instance)
export DEERFLOW_LANGGRAPH_URL=http://localhost:2024
mcp-server-deerflow-kinthaiThe server starts on port 8808 with SSE transport at /sse.
Requires Python >= 3.12.
Prerequisites
You need a running DeerFlow instance. Follow the DeerFlow README to set it up, then point this server at it:
# Default: DeerFlow LangGraph on localhost:2024
export DEERFLOW_LANGGRAPH_URL=http://localhost:2024
# Optional: DeerFlow Gateway for artifact downloads (charts, PPTs, images)
export DEERFLOW_GATEWAY_URL=http://localhost:8001Configuration
Environment Variables
Variable | Default | Description |
|
| DeerFlow LangGraph server URL |
|
| DeerFlow Gateway API URL (for artifact downloads) |
OpenClaw
Add to your openclaw.json:
{
"mcp": {
"servers": {
"deerflow-kinthai": {
"url": "http://localhost:8808/sse"
}
}
}
}Claude Desktop
Add to your Claude Desktop config:
{
"mcpServers": {
"deerflow-kinthai": {
"command": "mcp-server-deerflow-kinthai"
}
}
}Claude Code
claude mcp add deerflow-kinthai http://localhost:8808/sse --transport sseMount in Existing App
The server can be embedded in an existing FastAPI/Starlette application:
from fastapi import FastAPI
from mcp_server_deerflow_kinthai.server import create_starlette_app
app = FastAPI()
app.mount("/mcp", create_starlette_app())Development
git clone https://github.com/kinthaiofficial/mcp-server-deerflow-kinthai
cd mcp-server-deerflow-kinthai
pip install -e ".[dev]"
pytestRelated Projects
DeerFlow — The upstream multi-agent research framework by ByteDance
openclaw-kinthai — OpenClaw channel plugin for KinthAI
kinthai-agent-cli — Universal CLI bridge for connecting any agent to KinthAI
License
MIT — KinthAI
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