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OSINTMCPServer
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---
title: OSINTMCPServer
emoji: πŸ’¬
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.

---

## πŸ”Ή Mode B β€” 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.

---

## πŸ”Ή Mode D β€” 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:
	β€’	Raw JSON
	β€’	Rendered Markdown (if returned by the tool)

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

βΈ»

πŸ”Ή Mode C β€” Analyst Copilot (LLM)

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

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

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

βΈ»

πŸ—οΈ Architecture

OSINT Tasks β†’ Correlation/Enrichment β†’ MITRE Mapping β†’ Outputs β†’ Analyst Copilot

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

βΈ»

πŸš€ Running Locally

Install dependencies:

pip install -r requirements.txt

Run the app:

python app.py


βΈ»

πŸ” API Tokens

The Analyst Copilot uses the HuggingFace Inference API.

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

βΈ»

πŸ“¦ Repository Structure

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


βΈ»

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

βΈ»

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

---