MCP server that connects AI assistants to HackerOne for bug bounty hunting
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"h1-brain": {
"env": {
"H1_USERNAME": "your_hackerone_username",
"H1_API_TOKEN": "your_api_token"
},
"args": [
"/path/to/h1-brain/server.py"
],
"command": "/path/to/h1-brain/venv/bin/python"
}
}
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An MCP server that connects your AI assistant to HackerOne. It pulls your bug bounty history, program scopes, and report details into a local SQLite database, then exposes tools that let any MCP-compatible client (Claude Desktop, Claude Code, etc.) search, analyze, and build on your past work.
No automated test available for this server. Check the GitHub README for setup instructions.
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An MCP server that connects your AI assistant to HackerOne. It pulls your bug bounty history, program scopes, and report details into a local SQLite database, then exposes tools that let any MCP-compatible client (Claude Desktop, Claude Code, etc.) search, analyze, and build on your past work.
It also ships with a pre-built database of 3,600+ publicly disclosed bounty-awarded reports from the HackerOne community — full vulnerability write-ups, weakness types, and bounty amounts. The AI uses both your personal data and public knowledge to generate attack briefings.
The primary tool, hack(handle), generates a full hacking session briefing in a single call: fresh scope from the API, your past findings, public disclosures for that program, weakness patterns, untouched assets, and suggested attack vectors — all formatted as actionable instructions that put the AI in offensive mode.

For a full walkthrough, check out the three-part Bug Bounty Goldfish series:
hack() briefinggraph LR
A["Claude Desktop / Code"] -->|MCP Protocol| B["h1-brain server"]
B -->|API calls| C["HackerOne API"]
B -->|reads / writes| D["Your Reports DB"]
B -->|reads| E["Public Reports DB"]
C -->|reports, programs, scopes| B
D -->|your history + analysis| A
E -->|community knowledge| A
style A fill:#ff5c5c,stroke:#ff5c5c,color:#fff
style B fill:#1a1d27,stroke:#ff5c5c,color:#fff
style C fill:#1a1d27,stroke:#555,color:#fff
style D fill:#1a1d27,stroke:#555,color:#fff
style E fill:#1a1d27,stroke:#555,color:#fff
flowchart TD
A["hack(handle)"] --> B["Fetch fresh scope from HackerOne API"]
B --> C["Pull your reports on this program from SQLite"]
C --> D["Analyze weakness patterns across ALL programs"]
D --> E["Identify untouched bounty-eligible assets"]
E --> F["Cross-reference public disclosed reports for this program"]
F --> G["Generate attack briefing with agent instructions"]
style A fill:#ff5c5c,stroke:#ff5c5c,color:#fff
style G fill:#ff5c5c,stroke:#ff5c5c,color:#fff
style B fill:#1a1d27,stroke:#555,color:#fff
style C fill:#1a1d27,stroke:#555,color:#fff
style D fill:#1a1d27,stroke:#555,color:#fff
style E fill:#1a1d27,stroke:#555,color:#fff
style F fill:#1a1d27,stroke:#555,color:#fff
git clone https://github.com/PatrikFehrenbach/h1-brain.git
cd h1-brain
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
The public disclosed reports database (disclosed_reports.db) is included in the repo — no extra setup needed.
Add to ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"h1-brain": {
"command": "/path/to/h1-brain/venv/bin/python",
"args": ["/path/to/h1-brain/server.py"],
"env": {
"H1_USERNAME": "your_hackerone_username",
"H1_API_TOKEN": "your_api_token"
}
}
}
}
Restart Claude Desktop after saving.
claude mcp add h1-brain \
-e H1_USERNAME=your_hackerone_username \
-e H1_API_TOKEN=your_api_token \
-- /path/to/h1-brain/venv/bin/python /path/to/h1-brain/server.py
After connecting, populate your pers