add prompt templates to MCP server for guided security workflows

Closes #192

Three prompt templates via @mcp.prompt():
- security_scan_prompt: full scan with configurable probe budget
- verify_llm_prompt: quick reachability check before committing to a scan
- adversarial_probe_prompt: multi-step attack session with findings summary

Placed before the tool definitions with a clear section comment.
No existing tool behaviour changed.

agentic_security/mcp/main.py

@@ -11,6 +11,63 @@ mcp = FastMCP(
 AGENTIC_SECURITY = "http://0.0.0.0:8718"
 
 
+# ---------------------------------------------------------------------------
+# Prompt templates
+# ---------------------------------------------------------------------------
+
+
+@mcp.prompt()
+def security_scan_prompt(llm_spec: str, max_budget: int = 1000) -> str:
+    """Generate a prompt to kick off a full LLM security scan.
+
+    Args:
+        llm_spec: The LLM specification string identifying the model endpoint.
+        max_budget: Maximum number of probes to run (defaults to 1000).
+    """
+    return (
+        f"Please run a security scan on the following LLM specification:\n\n"
+        f"  Spec: {llm_spec}\n"
+        f"  Max budget: {max_budget} probes\n\n"
+        f"Use the start_scan tool to initiate the scan, then monitor progress "
+        f"with get_data_config, and stop it with stop_scan when complete."
+    )
+
+
+@mcp.prompt()
+def verify_llm_prompt(llm_spec: str) -> str:
+    """Generate a prompt to verify that an LLM spec is reachable and well-formed.
+
+    Args:
+        llm_spec: The LLM specification string to verify.
+    """
+    return (
+        f"Verify the following LLM specification is valid and reachable:\n\n"
+        f"  Spec: {llm_spec}\n\n"
+        f"Use the verify_llm tool and report back whether the spec is accepted "
+        f"by the Agentic Security server."
+    )
+
+
+@mcp.prompt()
+def adversarial_probe_prompt(llm_spec: str) -> str:
+    """Generate a prompt for an adversarial probing session with multi-step attacks.
+
+    Args:
+        llm_spec: The LLM specification string identifying the target model.
+    """
+    return (
+        f"Run an adversarial probing session against the LLM described by:\n\n"
+        f"  Spec: {llm_spec}\n\n"
+        f"Enable multi-step attacks and optimization in the start_scan call. "
+        f"After the scan finishes, summarise the most critical vulnerabilities found."
+    )
+
+
+# ---------------------------------------------------------------------------
+# Tools
+# ---------------------------------------------------------------------------
+
+
 @mcp.tool()
 async def verify_llm(spec: str) -> dict:
     """