Deep Research Mcp — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited Deep Research Mcp (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.
This repository provides a multi-agent research framework using Python and MCP (Message Control Protocol). The default entrypoint is mcp_server.py, which sets up a FastMCP server named Deep Research and exposes a tool named deep_research.
git clone https://github.com/yourusername/deep-research-mcp.git
cd deep-research-mcp cp .env.example .env
# Then edit the new .env file to fill in your secrets and environment variables
# For example:
# OPENAI_API_KEY=sk-123-yourkey
# Additional environment variables can be placed here pip install -r multi_agents/requirements.txtclaude_desktop_config.json file to include the following: {
"mcpServers": {
"deep-research-mcp": {
"command": "path/to/your/python/interpreter",
"args": [
"/path/to/this/project/deep-research-mcp/mcp_server.py"
]
}
}
} python mcp_server.pyThis starts the FastMCP tool server locally. From here, any MCP-compatible client or the CLI can invoke the deep_research tool.
Below is a copy of the multi_agents/README.md in a tree-like structure for reference:
multi_agents/
│
├─ README.md
│ └─ (Documentation on file output vs. direct return)
│
├─ agents/
│ ├─ __init__.py
│ ├─ browser.py
│ ├─ researcher.py
│ ├─ editor.py
│ ├─ writer.py
│ ├─ publisher.py
│ └─ ... (other agents)
│
├─ memory/
│ ├─ __init__.py
│ ├─ draft.py
│ └─ research.py
│
├─ main.py
├─ __init__.py
└─ requirements.txt~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.