README.md

---
title: OSINTMCPServer
emoji: 💬
colorFrom: yellow
colorTo: purple
sdk: gradio
sdk_version: 5.49.1
app_file: app.py
pinned: false
hf_oauth: true
hf_oauth_scopes:
- inference-api
license: apache-2.0
models: 
- berkeley-nest/WhiteRabbitNeo-8B
- cybertronai/cybertron-1.1-7b
datasets: 
- agentlans/HuggingFaceFW-finewiki-sample
- qywang1106/arxiv_number_small
- DanielPFlorian/Transformers-Github-Issues
- DanielPFlorian/Transformers-Github-Issues
- John6666/knowledge_base_md_for_rag_1
---

# Parrot OSINT MCP Console

A multi-mode OSINT analysis console built for structured intelligence workflows, streaming LLM analysis, and direct MCP tool access. Designed for investigation, enrichment, correlation, and report generation, all within a single Gradio interface.

---

## 🔹 Mode B — OSINT Dashboard

Interactive panels for:

- IP Lookup  
- Domain Lookup  
- Hash Lookup  
- IOC Correlation  
- Quickscan  
- MITRE ATT&CK Mapping  
- STIX / SARIF / JSON Output  

Each panel calls a corresponding MCP task and renders:

- Summary  
- Markdown report  
- Raw JSON  
- MITRE mappings  
- STIX bundles  

This is the structured-intelligence layer: deterministic, reproducible, and machine-readable.

---

## 🔹 Mode D — MCP Raw Bridge

Direct JSON-based invocation of any registered MCP task.

Example input:

```json
{
  "ip": "8.8.8.8",
  "enrich": true,
  "map_mitre": true
}

Output is shown as:
	•	Raw JSON
	•	Rendered Markdown (if returned by the tool)

This mode is ideal for debugging, development, automation, and power-user workflows.

⸻

🔹 Mode C — Analyst Copilot (LLM)

A streaming threat-intelligence assistant backed by the HuggingFace Inference API.

Capabilities include:
	•	Interpreting OSINT task results
	•	Drafting threat summaries
	•	Identifying TTPs, clusters, and adversary patterns
	•	Guiding step-by-step investigations
	•	Injecting dashboard/bridge results directly into conversation context

The copilot does not replace deterministic tasks — it explains them, contextualizes them, and synthesizes intelligence narratives.

⸻

🏗️ Architecture

OSINT Tasks → Correlation/Enrichment → MITRE Mapping → Outputs → Analyst Copilot

This separation keeps intelligence deterministic until you explicitly enter the interpretive layer.

⸻

🚀 Running Locally

Install dependencies:

pip install -r requirements.txt

Run the app:

python app.py


⸻

🔐 API Tokens

The Analyst Copilot uses the HuggingFace Inference API.

You can provide your token securely through the Gradio OAuthToken input inside the UI.

⸻

📦 Repository Structure

app.py
requirements.txt
README.md
runtime.txt      (optional)
hf.yaml          (optional)
.gitignore
tasks/           (your MCP tools)


⸻

📝 Notes
	•	Do not commit .mcp/secrets.json or any API keys.
	•	If MCP tasks depend on network-based OSINT sources (Shodan, Censys, VT, etc.), ensure rate limits and caching are configured.
	•	The UI is modular — you can add new tools to the registry without changing the interface.

⸻

Parrot OSINT MCP Console is built for analysts, builders, and anyone who needs intelligence workflows that scale across data sources, formats, and models.

---