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chore: delete MCP server and client (#308)

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@@ -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.