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README.md

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-An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).
+# 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.
+
+---
+