Mcp Server Deerflow Kinthai — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited Mcp Server Deerflow Kinthai (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
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.
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.
| Tool | Description |
|---|---|
deep_research | Multi-source web research with cross-verification |
data_analysis | Data analysis with DuckDB (CSV/Excel) |
chart_visualization | 26+ chart types (line, bar, pie, scatter, sankey, etc.) |
ppt_generation | PowerPoint presentation generation |
image_generation | AI image generation |
consulting_analysis | 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.
# 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.
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:8001| Variable | Default | Description |
|---|---|---|
DEERFLOW_LANGGRAPH_URL | http://localhost:2024 | DeerFlow LangGraph server URL |
DEERFLOW_GATEWAY_URL | http://localhost:8001 | DeerFlow Gateway API URL (for artifact downloads) |
Add to your openclaw.json:
{
"mcp": {
"servers": {
"deerflow-kinthai": {
"url": "http://localhost:8808/sse"
}
}
}
}Add to your Claude Desktop config:
{
"mcpServers": {
"deerflow-kinthai": {
"command": "mcp-server-deerflow-kinthai"
}
}
}claude mcp add deerflow-kinthai http://localhost:8808/sse --transport sseThe 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())git clone https://github.com/kinthaiofficial/mcp-server-deerflow-kinthai
cd mcp-server-deerflow-kinthai
pip install -e ".[dev]"
pytestMIT — KinthAI
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.