Panther MCP Server

Panther's Model Context Protocol (MCP) server provides functionality to:

1. Write and tune detections from your IDE
2. Interactively query security logs using natural language
3. Triage, comment, and resolve one or many alerts

<a href="https://glama.ai/mcp/servers/@panther-labs/mcp-panther">

</a>

Available Tools

<details>
<summary><strong>Alerts</strong></summary>

| Tool Name | Description | Sample Prompt |
|-----------|-------------|---------------|
| add_alert_comment | Add a comment to a Panther alert | "Add comment 'Looks pretty bad' to alert abc123" |
| start_ai_alert_triage | Start an AI-powered triage analysis for a Panther alert with intelligent insights and recommendations | "Start AI triage for alert abc123" / "Generate a detailed AI analysis of alert def456" |
| get_ai_alert_triage_summary | Retrieve the latest AI triage summary previously generated for a specific alert | "Get the AI triage summary for alert abc123" / "Show me the AI analysis for alert def456" |
| get_alert | Get detailed information about a specific alert | "What's the status of alert 8def456?" |
| get_alert_events | Get a small sampling of events for a given alert | "Show me events associated with alert 8def456" |
| list_alerts | List alerts with comprehensive filtering options (date range, severity, status, etc.) | "Show me all high severity alerts from the last 24 hours" |
| bulk_update_alerts | Bulk update multiple alerts with status, assignee, and/or comment changes | "Update alerts abc123, def456, and ghi789 to resolved status and add comment 'Fixed'" |
| update_alert_assignee | Update the assignee of one or more alerts | "Assign alerts abc123 and def456 to John" |
| update_alert_status | Update the status of one or more alerts | "Mark alerts abc123 and def456 as resolved" |
| list_alert_comments | List all comments for a specific alert | "Show me all comments for alert abc123" |

</details>

<details>
<summary><strong>Data Lake</strong></summary>

| Tool Name | Description | Sample Prompt |
|-----------|-------------|---------------|
| query_data_lake | Execute SQL queries against Panther's data lake with synchronous results | "Query AWS CloudTrail logs for failed login attempts in the last day" |
| get_table_schema | Get schema information for a specific table | "Show me the schema for the AWS_CLOUDTRAIL table" |
| list_databases | List all available data lake databases in Panther | "List all available databases" |
| list_database_tables | List all available tables for a specific database in Panther's data lake | "What tables are in the panther_logs database" |
| get_alert_event_stats | Analyze patterns and relationships across multiple alerts by aggregating their event data into time-based statistics | "Show me patterns in events from alerts abc123 and def456" |

</details>

<details>
<summary><strong>Scheduled Queries</strong></summary>

| Tool Name | Description | Sample Prompt |
|-----------|-------------|---------------|
| list_scheduled_queries | List all scheduled queries with pagination support | "Show me all scheduled queries" / "List the first 25 scheduled queries" |
| get_scheduled_query | Get detailed information about a specific scheduled query by ID | "Get details for scheduled query 'weekly-security-report'" |

</details>

<details>
<summary><strong>Sources</strong></summary>

| Tool Name | Description | Sample Prompt |
|-----------|-------------|---------------|
| list_log_sources | List log sources with optional filters (health status, log types, integration type) | "Show me all healthy S3 log sources" |
| get_http_log_source | Get detailed information about a specific HTTP log source by ID | "Show me the configuration for HTTP source 'webhook-collector-123'" |

</details>

<details>
<summary><strong>Detections</strong></summary>

| Tool Name | Description | Sample Prompt |
|-----------|-------------|---------------|
| list_detections | List detections from Panther with comprehensive filtering support. Supports multiple detection types and filtering by name, state, severity, tags, log types, resource types, output IDs (destinations), and more. Returns outputIDs for each detection showing configured alert destinations | "Show me all enabled HIGH severity rules with tag 'AWS'" / "List disabled policies for S3 resources" / "Find all rules with outputID 'prod-slack'" / "Show me detections that alert to production destinations" |
| get_detection | Get detailed information about a specific detection including the detection body and tests. Accepts a list with one detection type: ["rules"], ["scheduled_rules"], ["simple_rules"], or ["policies"] | "Get details for rule ID abc123" / "Get details for policy ID AWS.S3.Bucket.PublicReadACP" |
| disable_detection | Disable a detection by setting enabled to false. Supports rules, scheduled_rules, simple_rules, and policies | "Disable rule abc123" / "Disable policy AWS.S3.Bucket.PublicReadACP" |

</details>

<details>
<summary><strong>Global Helpers</strong></summary>

| Tool Name | Description | Sample Prompt |
|-----------|-------------|---------------|
| list_global_helpers | List global helper functions with comprehensive filtering options (name search, creator, modifier) | "Show me global helpers containing 'aws' in the name" |
| get_global_helper | Get detailed information and complete Python code for a specific global helper | "Get the complete code for global helper 'AWSUtilities'" |

</details>

<details>
<summary><strong>Data Models</strong></summary>

| Tool Name | Description | Sample Prompt |
|-----------|-------------|---------------|
| list_data_models | List data models that control UDM mappings in rules | "Show me all data models for log parsing" |
| get_data_model | Get detailed information about a specific data model | "Get the complete details for the 'AWS_CloudTrail' data model" |

</details>

<details>
<summary><strong>Schemas</strong></summary>

| Tool Name | Description | Sample Prompt |
|-----------|-------------|---------------|
| list_log_type_schemas | List available log type schemas with optional filters | "Show me all AWS-related schemas" |
| get_log_type_schema_details | Get detailed information for specific log type schemas | "Get full details for AWS.CloudTrail schema" |

</details>

<details>
<summary><strong>Metrics</strong></summary>

| Tool Name | Description | Sample Prompt |
|-----------|-------------|---------------|
| get_rule_alert_metrics | Get metrics about alerts grouped by rule | "Show top 10 rules by alert count" |
| get_severity_alert_metrics | Get metrics about alerts grouped by severity | "Show alert counts by severity for the last week" |
| get_bytes_processed_metrics | Get data ingestion metrics by log type and source | "Show me data ingestion volume by log type" |

</details>

<details>
<summary><strong>Users & Access Management</strong></summary>

| Tool Name | Description | Sample Prompt |
|-----------|-------------|---------------|
| list_users | List all Panther user accounts with pagination support | "Show me all active Panther users" / "List the first 25 users" |
| get_user | Get detailed information about a specific user | "Get details for user ID '<[email protected]>'" |
| get_permissions | Get the current user's permissions | "What permissions do I have?" |
| list_roles | List all roles with filtering options (name search, role IDs, sort direction) | "Show me all roles containing 'Admin' in the name" |
| get_role | Get detailed information about a specific role including permissions | "Get complete details for the 'Admin' role" |

</details>

Panther Configuration

Follow these steps to configure your API credentials and environment.

1. Create an API token in Panther:
- Navigate to Settings (gear icon) → API Tokens
- Create a new token with the following permissions (recommended read-only approach to start):
- <details>
<summary><strong>View Required Permissions</strong></summary>

</details>

2. Store the generated token securely (e.g., 1Password)

3. Copy the Panther instance URL from your browser (e.g., https://YOUR-PANTHER-INSTANCE.domain)
- Note: This must include https://

MCP Server Installation

Choose one of the following installation methods:

Docker (Recommended)

The easiest way to get started is using our pre-built Docker image:

{
  "mcpServers": {
    "mcp-panther": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "-e", "PANTHER_INSTANCE_URL",
        "-e", "PANTHER_API_TOKEN",
        "--rm",
        "ghcr.io/panther-labs/mcp-panther"
      ],
      "env": {
        "PANTHER_INSTANCE_URL": "https://YOUR-PANTHER-INSTANCE.domain",
        "PANTHER_API_TOKEN": "YOUR-API-KEY"
      }
    }
  }
}

Version Pinning: For production stability, pin to a specific version tag:

"ghcr.io/panther-labs/mcp-panther:v2.2.0"

Available tags can be found on the GitHub Container Registry.

UVX

For Python users, you can run directly from PyPI using uvx:

1. Install UV

2. Configure your MCP client:

{
  "mcpServers": {
    "mcp-panther": {
      "command": "uvx",
      "args": ["mcp-panther"],
      "env": {
        "PANTHER_INSTANCE_URL": "https://YOUR-PANTHER-INSTANCE.domain",
        "PANTHER_API_TOKEN": "YOUR-PANTHER-API-TOKEN"
      }
    }
  }
}

Version Pinning: For production stability, pin to a specific version:

"args": ["mcp-panther==2.2.0"]

Available versions can be found on PyPI.

MCP Client Setup

Cursor

Follow the instructions here to configure your project or global MCP configuration. It's VERY IMPORTANT that you do not check this file into version control.

Once configured, navigate to Cursor Settings > MCP to view the running server:

Tips:

- Be specific about where you want to generate new rules by using the @ symbol and then typing a specific directory.
- For more reliability during tool use, try selecting a specific model, like Claude 3.7 Sonnet.
- If your MCP Client is failing to find any tools from the Panther MCP Server, try restarting the Client and ensuring the MCP server is running. In Cursor, refresh the MCP Server and start a new chat.

Claude Code

Claude Code is Anthropic's official CLI tool. Add the Panther MCP server using Docker:

claude mcp add-json panther '{
  "command": "docker",
  "args": [
    "run",
    "-i",
    "-e", "PANTHER_INSTANCE_URL",
    "-e", "PANTHER_API_TOKEN",
    "--rm",
    "ghcr.io/panther-labs/mcp-panther"
  ],
  "env": {
    "PANTHER_INSTANCE_URL": "https://YOUR-PANTHER-INSTANCE.domain",
    "PANTHER_API_TOKEN": "YOUR-API-TOKEN"
  }
}'

Alternatively, using UVX:

claude mcp add-json panther '{
  "command": "uvx",
  "args": ["mcp-panther"],
  "env": {
    "PANTHER_INSTANCE_URL": "https://YOUR-PANTHER-INSTANCE.domain",
    "PANTHER_API_TOKEN": "YOUR-API-TOKEN"
  }
}'

After adding, verify the server is configured:

claude mcp list

Claude Desktop

To use with Claude Desktop, manually configure your claude_desktop_config.json:

1. Open the Claude Desktop settings and navigate to the Developer tab
2. Click "Edit Config" to open the configuration file
3. Add the following configuration:

{
  "mcpServers": {
    "mcp-panther": {
      "command": "uvx",
      "args": ["mcp-panther"],
      "env": {
        "PANTHER_INSTANCE_URL": "https://YOUR-PANTHER-INSTANCE.domain",
        "PANTHER_API_TOKEN": "YOUR-PANTHER-API-TOKEN"
      }
    }
  }
}

4. Save the file and restart Claude Desktop

If you run into any issues, try the troubleshooting steps here.

Goose CLI

Use with Goose CLI, Block's open-source AI agent:

```bash