Content
# MCP Scanner
A Python tool for scanning MCP (Model Context Protocol) servers and tools for potential security findings. The MCP Scanner combines Cisco AI Defense inspect API, YARA rules and LLM-as-a-judge to detect malicious MCP tools.
## Overview
The MCP Scanner provides a comprehensive solution for scanning MCP servers and tools for security findings. It leverages three powerful scanning engines (Yara, LLM-as-judge, Cisco AI Defense) that can be used together or independently.
The SDK is designed to be easy to use while providing powerful scanning capabilities, flexible authentication options, and customization.
## Features
- **Multiple Modes:** Run scanner as a stand-alone CLI tool or REST API server
- **Multi-Engine Security Analysis**: Use all three scanning engines together or independently based on your needs.
- **Comprehensive Scanning**: Scan MCP tools, prompts, and resources for security findings
- **Explicit Authentication Control**: Fine-grained control over authentication with explicit Auth parameters.
- **OAuth Support**: Full OAuth authentication support for both SSE and streamable HTTP connections.
- **Custom Endpoints**: Configure the API endpoint to support any Cisco AI Defense environments.
- **MCP Server Integration**: Connect directly to MCP servers to scan tools, prompts, and resources with flexible authentication.
- **Customizable YARA Rules**: Add your own YARA rules to detect specific patterns.
- **Comprehensive Reporting**: Detailed reports on detected security findings.
## Installation
### Prerequisites
- Python 3.11+
- uv (Python package manager)
- A valid Cisco AI Defense API Key (optional)
- LLM Provider API Key (optional)
### Installing from PyPI
```bash
uv venv -p <Python version less than or equal to 3.13> /path/to/your/choice/of/venv/directory
source /path/to/your/choice/of/venv/directory/bin/activate
uv pip install cisco-ai-mcp-scanner
```
### Installing from Source
```bash
git clone https://github.com/cisco-ai-defense/mcp-scanner
cd mcp-scanner
# Install with uv (recommended)
uv venv -p <Python version less than or equal to 3.13> /path/to/your/choice/of/venv/directory
source /path/to/your/choice/of/venv/directory/bin/activate
uv pip install .
# Or install in development mode
uv pip install -e .
```
## Quick Start
### Environment Setup
#### Core API Configuration
```bash
Cisco AI Defense API (only required for API analyzer)
export MCP_SCANNER_API_KEY="your_cisco_api_key"
export MCP_SCANNER_ENDPOINT="https://us.api.inspect.aidefense.security.cisco.com/api/v1"
# For other endpoints please visit https://developer.cisco.com/docs/ai-defense/getting-started/#base-url
```
#### LLM Configuration (for LLM analyzer)
**Tested LLMs:** OpenAI GPT-4o and GPT-4.1
```bash
# AWS Bedrock Claude with AWS credentials (profile)
export AWS_PROFILE="your-profile"
export AWS_REGION="us-east-1"
export MCP_SCANNER_LLM_MODEL="bedrock/anthropic.claude-sonnet-4-5-20250929-v2:0" # Any AWS Bedrock supported model
# AWS Bedrock Claude with API key (Bearer token)
export MCP_SCANNER_LLM_API_KEY="bedrock-api-key-..." # Generated via Amazon Bedrock -> API Keys
export AWS_REGION="us-east-1"
export MCP_SCANNER_LLM_MODEL="bedrock/us.anthropic.claude-sonnet-4-5-20250929-v2:0" # Any AWS Bedrock supported model
# LLM Provider API Key (required for LLM analyzer)
export MCP_SCANNER_LLM_API_KEY="your_llm_api_key" # OpenAI
# LLM Model Configuration (optional - defaults provided)
export MCP_SCANNER_LLM_MODEL="gpt-4o" # Any LiteLLM-supported model
export MCP_SCANNER_LLM_BASE_URL="https://api.openai.com/v1" # Custom LLM endpoint
export MCP_SCANNER_LLM_API_VERSION="2024-02-01" # API version (if required)
# For Azure OpenAI (example)
export MCP_SCANNER_LLM_BASE_URL="https://your-resource.openai.azure.com/"
export MCP_SCANNER_LLM_API_VERSION="2024-02-01"
export MCP_SCANNER_LLM_MODEL="azure/gpt-4"
# For Extended Thinking Models (longer timeout)
export MCP_SCANNER_LLM_TIMEOUT=300
```
#### Using a Local LLM (No API Key Required)
If you are using a local LLM endpoint such as Ollama, vLLM, or LocalAI,
the `MCP_SCANNER_LLM_API_KEY` variable is still required but can be set to any value.
Example:
```bash
export MCP_SCANNER_LLM_API_KEY=test
export MCP_SCANNER_LLM_ENDPOINT=http://localhost:11434
```
### Quick Start Examples
The fastest way to get started is using the `mcp-scanner` CLI command. Global flags (like `--analyzers`, `--format`, etc.) must be placed before a subcommand.
#### CLI Usage
```bash
# Scan well-known client configs on this machine
mcp-scanner --scan-known-configs --analyzers yara --format summary
# Stdio server (example using uvx mcp-server-fetch)
mcp-scanner --stdio-command uvx --stdio-arg=--from --stdio-arg=mcp-server-fetch --stdio-arg=mcp-server-fetch --analyzers yara --format summary
# Remote server (deepwiki example)
mcp-scanner --server-url https://mcp.deepwki.com/mcp --analyzers yara --format summary
# MCP Scanner as REST API
mcp-scanner-api --host 0.0.0.0 --port 8080
```
#### SDK Usage
```python
import asyncio
from mcpscanner import Config, Scanner
from mcpscanner.core.models import AnalyzerEnum
async def main():
# Create configuration with your API keys
config = Config(
api_key="your_cisco_api_key",
llm_provider_api_key="your_llm_api_key"
)
# Create scanner
scanner = Scanner(config)
# Scan all tools on a remote server
tool_results = await scanner.scan_remote_server_tools(
"https://mcp.deepwki.com/mcp",
analyzers=[AnalyzerEnum.API, AnalyzerEnum.YARA, AnalyzerEnum.LLM]
)
# Print tool results
for result in tool_results:
print(f"Tool: {result.tool_name}, Safe: {result.is_safe}")
# Scan all prompts on a server
prompt_results = await scanner.scan_remote_server_prompts(
"http://127.0.0.1:8000/mcp",
analyzers=[AnalyzerEnum.LLM]
)
# Print prompt results
for result in prompt_results:
print(f"Prompt: {result.prompt_name}, Safe: {result.is_safe}")
# Scan all resources on a server
resource_results = await scanner.scan_remote_server_resources(
"http://127.0.0.1:8000/mcp",
analyzers=[AnalyzerEnum.LLM],
allowed_mime_types=["text/plain", "text/html"]
)
# Print resource results
for result in resource_results:
print(f"Resource: {result.resource_name}, Safe: {result.is_safe}, Status: {result.status}")
# Run the scanner
asyncio.run(main())
```
#### Subcommands Overview
- **remote**: scan a remote MCP server (SSE or streamable HTTP). Supports `--server-url`, optional `--bearer-token`.
- **stdio**: launch and scan a stdio MCP server. Requires `--stdio-command`; accepts `--stdio-args`, `--stdio-env`, optional `--stdio-tool`.
- **config**: scan servers from a specific MCP config file. Requires `--config-path`; optional `--bearer-token`.
- **known-configs**: scan servers from well-known client config locations on this machine; optional `--bearer-token`.
- **prompts**: scan prompts on an MCP server. Requires `--server-url`; optional `--prompt-name`, `--bearer-token`.
- **resources**: scan resources on an MCP server. Requires `--server-url`; optional `--resource-uri`, `--mime-types`, `--bearer-token`.
Note: Top-level flags (e.g., `--server-url`, `--stdio-*`, `--config-path`, `--scan-known-configs`) remain supported when no subcommand is used, but subcommands are recommended.
#### Additional Examples
#### Scan well-known MCP config paths (Windsurf, Cursor, Claude, VS Code)
```bash
# YARA-only scan of all servers defined in well-known config locations
mcp-scanner --scan-known-configs --analyzers yara --format summary
# Detailed output
mcp-scanner --scan-known-configs --analyzers yara --detailed
```
#### Scan a specific MCP config file
```bash
# Expand ~ yourself if needed by your shell
mcp-scanner --config-path "$HOME/.codeium/windsurf/mcp_config.json" \
--analyzers yara --format by_tool
```
#### Scan a stdio MCP server
```bash
# Use repeated --stdio-arg for reliable argument passing
mcp-scanner --analyzers yara --format summary \
stdio --stdio-command uvx \
--stdio-arg=--from --stdio-arg=mcp-server-fetch --stdio-arg=mcp-server-fetch
# Or list-form (ensure it doesn't conflict with later flags)
mcp-scanner --analyzers yara --detailed \
stdio --stdio-command uvx \
--stdio-args --from mcp-server-fetch mcp-server-fetch
# Scan only a specific tool on the stdio server
mcp-scanner --analyzers yara --format summary \
stdio --stdio-command uvx \
--stdio-arg=--from --stdio-arg=mcp-server-fetch --stdio-arg=mcp-server-fetch \
--stdio-tool fetch
```
#### Use a Bearer token with remote servers (non-OAuth)
```bash
# Direct remote server with Bearer token
mcp-scanner --analyzers yara --format summary \
remote --server-url https://your-mcp-server/sse --bearer-token "$TOKEN"
# Apply Bearer token to all remote servers discovered from configs
mcp-scanner --analyzers yara --detailed known-configs --bearer-token "$TOKEN"
mcp-scanner --analyzers yara --format by_tool \
config --config-path "$HOME/.codeium/windsurf/mcp_config.json" --bearer-token "$TOKEN"
```
#### Scan Prompts
```bash
# Scan all prompts on an MCP server
mcp-scanner --analyzers llm prompts --server-url http://127.0.0.1:8000/mcp
# Scan all prompts with detailed output
mcp-scanner --analyzers llm --detailed prompts --server-url http://127.0.0.1:8000/mcp
# Scan all prompts with table format
mcp-scanner --analyzers llm --format table prompts --server-url http://127.0.0.1:8000/mcp
# Scan a specific prompt by name
mcp-scanner --analyzers llm prompts --server-url http://127.0.0.1:8000/mcp --prompt-name "greet_user"
# Get raw JSON output
mcp-scanner --analyzers llm --raw prompts --server-url http://127.0.0.1:8000/mcp
```
#### Scan Resources
```bash
# Scan all resources on an MCP server
mcp-scanner --analyzers llm resources --server-url http://127.0.0.1:8000/mcp
# Scan all resources with detailed output
mcp-scanner --analyzers llm --detailed resources --server-url http://127.0.0.1:8000/mcp
# Scan all resources with table format
mcp-scanner --analyzers llm --format table resources --server-url http://127.0.0.1:8000/mcp
# Scan a specific resource by URI
mcp-scanner --analyzers llm resources --server-url http://127.0.0.1:8000/mcp \
--resource-uri "file://test/document.txt"
# Scan with custom MIME type filtering
mcp-scanner --analyzers llm resources --server-url http://127.0.0.1:8000/mcp \
--mime-types "text/plain,text/html,application/json"
```
### API Server Usage
The API server provides a REST interface to the MCP scanner functionality, allowing you to integrate security scanning into web applications, CI/CD pipelines, or other services. It exposes the same scanning capabilities as the CLI tool but through HTTP endpoints.
```bash
# Start the API server (loads configuration from .env file)
mcp-scanner-api --port 8000
# Or with custom host and port
mcp-scanner-api --host 0.0.0.0 --port 8080
# Enable development mode with auto-reload
mcp-scanner-api --reload
```
Once running, the API server provides endpoints for:
- **`/scan-tool`** - Scan a specific tool on an MCP server
- **`/scan-all-tools`** - Scan all tools on an MCP server
- **`/scan-prompt`** - Scan a specific prompt on an MCP server
- **`/scan-all-prompts`** - Scan all prompts on an MCP server
- **`/scan-resource`** - Scan a specific resource on an MCP server
- **`/scan-all-resources`** - Scan all resources on an MCP server
- **`/health`** - Health check endpoint
Documentation is available in [docs/api-reference.md](https://github.com/cisco-ai-defense/mcp-scanner/tree/main/docs/api-reference.md) or as interactive documentation at `http://localhost:8000/docs` when the server is running.
## Output Formats
The scanner supports multiple output formats:
- **`summary`**: Concise overview with key findings
- **`detailed`**: Comprehensive analysis with full findings breakdown
- **`table`**: Clean tabular format
- **`by_severity`**: Results grouped by severity level
- **`raw`**: Raw JSON output
### Example Output
#### Detailed Format
```bash
mcp-scanner --server-url http://127.0.0.1:8001/sse --format detailed
```
```
=== MCP Scanner Detailed Results ===
Scan Target: http://127.0.0.1:8001/sse
Tool: execute_system_command
Status: completed
Safe: No
Analyzer Results:
• api_analyzer:
- Severity: HIGH
- Threat Summary: Detected 1 threat: security violation
- Threat Names: SECURITY VIOLATION
- Total Findings: 1
• yara_analyzer:
- Severity: HIGH
- Threat Summary: Detected 2 threats: system access, command injection
- Threat Names: SECURITY VIOLATION, SUSPICIOUS CODE EXECUTION
- Total Findings: 2
• llm_analyzer:
- Severity: HIGH
- Threat Summary: Detected 2 threats: prompt injection, tool poisoning
- Threat Names: PROMPT INJECTION, SUSPICIOUS CODE EXECUTION
- Total Findings: 2
```
#### Table Format
```bash
mcp-scanner --server-url http://127.0.0.1:8002/sse --format table
```
```
=== MCP Scanner Results Table ===
Scan Target: http://127.0.0.1:8002/sse
Scan Target Tool Name Status API YARA LLM Severity
-----------------------------------------------------------------------------------------
http://127.0.0.1:8002/sse exec_secrets UNSAFE HIGH HIGH HIGH HIGH
http://127.0.0.1:8002/sse safe_command SAFE SAFE SAFE SAFE SAFE
```
## Documentation
For detailed documentation, see the [docs/](https://github.com/cisco-ai-defense/mcp-scanner/tree/main/docs) directory:
- **[Architecture](https://github.com/cisco-ai-defense/mcp-scanner/tree/main/docs/architecture.md)** - System architecture and components
- **[Authentication](https://github.com/cisco-ai-defense/mcp-scanner/tree/main/docs/authentication.md)** - OAuth and security configuration
- **[Programmatic Usage](https://github.com/cisco-ai-defense/mcp-scanner/tree/main/docs/programmatic-usage.md)** - Programmatic usage examples and advanced usage
- **[API Reference](https://github.com/cisco-ai-defense/mcp-scanner/tree/main/docs/api-reference.md)** - Complete REST API documentation
- **[Output Formats](https://github.com/cisco-ai-defense/mcp-scanner/tree/main/docs/output-formats.md)** - Detailed output format options
## Contact Cisco for obtaining an AI Defense subscription
https://www.cisco.com/site/us/en/products/security/ai-defense/index.html
## License
Distributed under the `Apache 2.0` License. See [LICENSE](https://github.com/cisco-ai-defense/mcp-scanner/tree/main/LICENSE) for more information.
Project Link: [https://github.com/cisco-ai-defense/mcp-scanner](https://github.com/cisco-ai-defense/mcp-scanner)
