chore: delete MCP server and client

Readme.md

@@ -68,25 +68,6 @@ agentic_security --port=PORT --host=HOST
 
 <img width="100%" alt="booking-screen" src="https://raw.githubusercontent.com/msoedov/agentic_security/refs/heads/main/docs/images/demo.gif">
 
-## MCP client example
-
-Agentic Security includes an MCP stdio server in `agentic_security.mcp.main`.
-To list the available MCP tools from a local checkout:
-
-```shell
-python examples/mcp_client_usage.py
-```
-
-To call HTTP-backed tools, run the Agentic Security app first, then point the
-MCP server at it:
-
-```shell
-agentic_security --host 127.0.0.1 --port 8718
-python examples/mcp_client_usage.py --agentic-security-url http://127.0.0.1:8718 --call get_spec_templates
-```
-
-See `docs/mcp_client_usage.md` for the full walkthrough.
-
 ## LLM kwargs
 
 Agentic Security uses plain text HTTP spec like:
@@ -405,68 +386,6 @@ This setup ensures a continuous integration approach towards maintaining securit
 The `Module` class is designed to manage prompt processing and interaction with external AI models and tools. It supports fetching, processing, and posting prompts asynchronously for model vulnerabilities. Check out [module.md](https://github.com/msoedov/agentic_security/blob/main/docs/module.md) for details.
 
 
-## MCP server
-
-The Agentic Security MCP server exposes the scanner's REST API as callable tools and reusable prompt templates, so any MCP-compatible client (Claude Desktop, Claude Code, custom agents) can drive security scans through natural language.
-
-### Installation
-
-```shell
-pip install -U mcp
-
-# From cloned directory
-mcp install agentic_security/mcp/main.py
-```
-
-### Using with Claude Desktop
-
-1. Start the Agentic Security FastAPI server (default port `8718`):
-
-   ```shell
-   poetry run agentic_security
-   ```
-
-2. Install the MCP server into Claude Desktop:
-
-   ```shell
-   mcp install agentic_security/mcp/main.py --name "Agentic Security"
-   ```
-
-3. Open Claude Desktop — the following **tools** are now available:
-
-   | Tool | Description |
-   |---|---|
-   | `start_scan` | Launch a security scan against an LLM spec |
-   | `stop_scan` | Halt an in-progress scan |
-   | `verify_llm` | Check that an LLM spec is reachable |
-   | `get_data_config` | Retrieve the current dataset configuration |
-   | `get_spec_templates` | List available LLM spec templates |
-
-4. Or kick off a scan using one of the built-in **prompt templates**:
-
-   - **`security_scan_prompt`** — runs a full scan with a configurable probe budget
-   - **`verify_llm_prompt`** — confirms a spec is reachable before committing to a scan
-   - **`adversarial_probe_prompt`** — enables multi-step attacks and asks Claude to summarise the worst findings
-
-### Example conversation with Claude
-
-```
-You: Use the security_scan_prompt for spec "openai/gpt-4o" with a budget of 500 probes.
-
-Claude: I'll kick off the scan now. Starting with verify_llm to confirm the spec is
-        reachable, then launching start_scan with maxBudget=500...
-```
-
-### Using with Claude Code (CLI)
-
-```shell
-# Add to your project's MCP config
-claude mcp add agentic-security -- python agentic_security/mcp/main.py
-
-# Then interact inline
-claude "Run a quick adversarial probe against my local LLM at http://localhost:8080/v1"
-```
-
 ## Documentation
 
 For more detailed information on how to use Agentic Security, including advanced features and customization options, please refer to the official documentation.