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Enables AI assistants to manage and troubleshoot PingOne DaVinci identity orchestration flows, applications, connectors, variables, and forms through standardiz
Enables AI assistants to manage and troubleshoot PingOne DaVinci identity orchestration flows, applications, connectors, variables, and forms through standardized MCP tools.
A Model Context Protocol (MCP) server that provides AI assistants with seamless access to PingOne's DaVinci identity orchestration platform resources. This server enables AI models to interact with DaVinci flows, applications, connectors, variables, and forms through a standardized interface.
[!WARNING] SECURITY & LEAST PRIVILEGE: This server grants an AI model significant access to your DaVinci environment configuration. All data returned from tools may be sent to the LLM provider.
- Use ONLY with trusted MCP clients.
- Follow the Principle of Least Privilege: Ensure the authenticating user is assigned a role with the minimum necessary permissions (e.g.,
DaVinci Admin Read Only).- HUMAN REVIEW REQUIRED: Always review all AI-generated configurations or insights before applying them to a live environment.
DaVinci is PingOne's no-code identity orchestration platform that allows organizations to build sophisticated identity and access management workflows. This MCP server acts as a bridge between MCP-compatible AI assistants and the DaVinci API, enabling:
The server provides the following MCP tools organized into two collections:
davinci_adminCore administrative tools for managing DaVinci resources (applications, flows, connectors, variables, forms).
Application Tools
| Tool | Description |
|---|---|
list_applications |
Returns a list of all DaVinci applications. |
describe_application |
Returns details of a single DaVinci application by ID. |
list_application_flow_policies |
Returns all flow policies for a DaVinci application. |
describe_application_flow_policy |
Returns details of a single flow policy for a DaVinci application. |
Flow Tools
| Tool | Description |
|---|---|
list_flows |
Returns a list of all DaVinci flows. Supports attributes to project the response to specific top-level fields. Flow type is derived from the trigger field: no trigger = standard flow; trigger.type AUTHENTICATION = PingOne flow; trigger.type AUTHENTICATION + trigger.subtype CIBA = CIBA flow; trigger.type SCHEDULE = scheduled flow; trigger.type BATCH_PROCESSING_SUBFLOW = batch processing subflow. readOnly: true means the flow is read-only. |
describe_flow |
Returns the complete definition of a DaVinci flow including the full node graph, edges, and settings. Use when auditing or understanding a flow's internal logic. Supports attributes to project the response to specific top-level fields and expand to include related fields inline (e.g. "dvlinterDetails"). |
list_flow_versions |
Returns all versions of a specific DaVinci flow. |
describe_flow_version |
Returns the complete definition of a specific DaVinci flow version, including the full node graph, edges, settings, and trigger configuration. Supports expand to include related fields inline (e.g. skcomponents). |
Connector Tools
| Tool | Description |
|---|---|
list_connectors |
Returns a list of all available DaVinci connector types from the catalog. |
describe_connector |
Returns the full details of a single DaVinci connector type by ID, including metadata, capabilities, configurable properties, and required credentials. |
list_connector_instances |
Returns a list of all deployed DaVinci connector instances. |
describe_connector_instance |
Returns details of a single deployed DaVinci connector instance by ID. |
Variable Tools
| Tool | Description |
|---|---|
list_variables |
Returns a list of all DaVinci variables. Supports limit (1–50), cursor for pagination, and a SCIM filter to narrow results. |
describe_variable |
Returns details of a single DaVinci variable by ID. |
Form Tools
| Tool | Description |
|---|---|
list_forms |
Returns a list of all DaVinci forms. Use for discovery and finding form IDs. Use describe_form for field-level details. Supports a SCIM filter on category (eq). |
describe_form |
Returns full configuration of a single DaVinci form including fields and layout. |
davinci_flow_troubleshootingSpecialized tools for flow validation, execution monitoring, and debugging.
Flow Troubleshooting Tools
| Tool | Description |
|---|---|
validate_flow |
Validates a DaVinci flow configuration using the DVLinter validation engine. Use this tool to check deployment readiness, identify configuration errors and warnings (best-practice violations), and troubleshoot flow issues. Analyzes nodes (connectors and capabilities), connections (connector instances), node properties, and overall structure. Returns validation results including error counts or warning counts, and specific issue descriptions. Error locations: (1) linterError property within each node in graphData.elements.nodes for node specific issues (2) allLinterErrors property in graphData for all flow-level errors and warnings. Zero errors indicates deployment-ready status. This is a read-only operation that does not modify the flow. |
list_flow_executions |
Returns a list of all executions for a specific DaVinci flow. Use this tool to find execution IDs for troubleshooting, debugging, or monitoring flow executions. Supports limit (max 500) and cursor for pagination and SCIM filter on timestamp (ge, le) with ISO 8601 dates, transactionId (eq) for specific transaction details. |
summarize_flow_execution |
Returns detailed information about a specific DaVinci flow execution with status (success/failure), timestamps, input/output data, errors with stack traces, and user context. Use this tool to debug failures, summarize flow execution results, analyze execution behavior, verify data transformations, or investigate user-specific issues. Supports limit (max 500) and cursor for pagination and SCIM filter on timestamp (ge, le) with ISO 8601 dates. |
The server uses OAuth 2.0 Authorization Code flow with PKCE for secure authentication:
keytar).The MCP server requires a Worker Application to authenticate with the PingOne API.
DaVinci MCP Server.http://127.0.0.1:7474/callback.AUTHORIZATION_CODE_CLIENT_ID variable.The authenticating user must have the necessary permissions to access DaVinci resources.
The server requires the following environment variables. These should be configured in your MCP client's settings (see Usage below).
| Variable | Description | Example |
|---|---|---|
DAVINCI_MCP_ENVIRONMENT_ID |
The ID of your PingOne environment. | a1b2c3d4-e5f6-g7h8-i9j0-k1l2m3n4o5p6 |
AUTHORIZATION_CODE_CLIENT_ID |
The Client ID of your PingOne Worker Application. | your-client-id |
ROOT_DOMAIN |
The regional PingOne domain. | pingone.com (NA), pingone.eu (EU), pingone.asia (APAC) |
CUSTOM_DOMAIN |
(Optional) Your custom PingOne domain. | auth.example.com |
Replace your-environment-id and your-client-id with your actual PingOne environment ID and OAuth Client ID in the examples below.
Add the following to your claude_desktop_config.json:
~/Library/Application Support/Claude/claude_desktop_config.json%APPDATA%\Claude\claude_desktop_config.json{
"mcpServers": {
"davinci": {
"command": "npx",
"args": ["-y", "@ping-identity/davinci-mcp-server", "start"],
"env": {
"DAVINCI_MCP_ENVIRONMENT_ID": "your-environment-id",
"AUTHORIZATION_CODE_CLIENT_ID": "your-client-id",
"ROOT_DOMAIN": "pingone.com"
}
}
}
}
Run the following command in your terminal:
claude mcp add --transport stdio davinci \
--env DAVINCI_MCP_ENVIRONMENT_ID="your-environment-id" \
--env AUTHORIZATION_CODE_CLIENT_ID="your-client-id" \
--env ROOT_DOMAIN="pingone.com" \
-- npx -y @ping-identity/davinci-mcp-server start
{
"davinci": {
"command": "npx",
"args": ["-y", "@ping-identity/davinci-mcp-server", "start"],
"env": {
"DAVINCI_MCP_ENVIRONMENT_ID": "your-environment-id",
"AUTHORIZATION_CODE_CLIENT_ID": "your-client-id",
"ROOT_DOMAIN": "pingone.com"
}
}
}
davincicommandnpx -y @ping-identity/davinci-mcp-server start
DAVINCI_MCP_ENVIRONMENT_ID: your-environment-idAUTHORIZATION_CODE_CLIENT_ID: your-client-idROOT_DOMAIN: pingone.comAdd the following to your ~/.gemini/settings.json:
{
"mcpServers": {
"davinci": {
"command": "npx",
"args": ["-y", "@ping-identity/davinci-mcp-server", "start"],
"env": {
"DAVINCI_MCP_ENVIRONMENT_ID": "your-environment-id",
"AUTHORIZATION_CODE_CLIENT_ID": "your-client-id",
"ROOT_DOMAIN": "pingone.com"
}
}
}
}
The server requires a command to execute and supports several flags to customize its behavior. These should be added to the args array in your MCP client configuration:
start: Initiates the MCP server (required for normal operation).--include-collections <list>: Comma-separated list of collection names to include (e.g., davinci_admin, davinci_flow_troubleshooting).--exclude-collections <list>: Comma-separated list of collection names to exclude.--include-tools <list>: Comma-separated list of tool names to include.--exclude-tools <list>: Comma-separated list of tool names to exclude.--verbose: Enable verbose logging to stderr.--logout: Trigger a logout flow on startup by clearing stored tokens.--help: Show the help message.These examples show how to configure claude_desktop_config.json for different scenarios. Change accordingly for other providers
{
"mcpServers": {
"davinci": {
"command": "npx",
"args": ["-y", "@ping-identity/davinci-mcp-server", "start"],
"env": {
"DAVINCI_MCP_ENVIRONMENT_ID": "your-environment-id",
"AUTHORIZATION_CODE_CLIENT_ID": "your-client-id",
"ROOT_DOMAIN": "pingone.com"
}
}
}
}
{
"mcpServers": {
"davinci": {
"command": "npx",
"args": [
"-y",
"@ping-identity/davinci-mcp-server",
"start",
"--include-collections",
"davinci_admin"
],
"env": {
"DAVINCI_MCP_ENVIRONMENT_ID": "your-environment-id",
"AUTHORIZATION_CODE_CLIENT_ID": "your-client-id",
"ROOT_DOMAIN": "pingone.com"
}
}
}
}
{
"mcpServers": {
"davinci": {
"command": "npx",
"args": [
"-y",
"@ping-identity/davinci-mcp-server",
"start",
"--include-collections",
"davinci_flow_troubleshooting"
],
"env": {
"DAVINCI_MCP_ENVIRONMENT_ID": "your-environment-id",
"AUTHORIZATION_CODE_CLIENT_ID": "your-client-id",
"ROOT_DOMAIN": "pingone.com"
}
}
}
}
{
"mcpServers": {
"davinci": {
"command": "npx",
"args": [
"-y",
"@ping-identity/davinci-mcp-server",
"start",
"--include-tools",
"list_flows,describe_flow"
],
"env": {
"DAVINCI_MCP_ENVIRONMENT_ID": "your-environment-id",
"AUTHORIZATION_CODE_CLIENT_ID": "your-client-id",
"ROOT_DOMAIN": "pingone.com"
}
}
}
}
{
"mcpServers": {
"davinci": {
"command": "npx",
"args": [
"-y",
"@ping-identity/davinci-mcp-server",
"start",
"--exclude-tools",
"describe_variable,list_variables"
],
"env": {
"DAVINCI_MCP_ENVIRONMENT_ID": "your-environment-id",
"AUTHORIZATION_CODE_CLIENT_ID": "your-client-id",
"ROOT_DOMAIN": "pingone.com"
}
}
}
}
{
"mcpServers": {
"davinci": {
"command": "npx",
"args": ["-y", "@ping-identity/davinci-mcp-server", "start", "--verbose"],
"env": {
"DAVINCI_MCP_ENVIRONMENT_ID": "your-environment-id",
"AUTHORIZATION_CODE_CLIENT_ID": "your-client-id",
"ROOT_DOMAIN": "pingone.com"
}
}
}
}
These commands are intended to be run manually in your terminal for maintenance or discovery.
npx -y @ping-identity/davinci-mcp-server start --logout
npx -y @ping-identity/davinci-mcp-server --help
Example (Claude Desktop Configuration):
{
"mcpServers": {
"davinci": {
"command": "npx",
"args": [
"-y",
"@ping-identity/davinci-mcp-server",
"start",
"--include-tools",
"list_flows,describe_flow",
"--verbose"
],
"env": {
"DAVINCI_MCP_ENVIRONMENT_ID": "your-environment-id",
"AUTHORIZATION_CODE_CLIENT_ID": "your-client-id",
"ROOT_DOMAIN": "pingone.com"
}
}
}
}
git clone https://github.com/pingidentity/davinci-mcp-server.git
cd davinci-mcp-server
npm ci
See the Development Workflow table in CONTRIBUTING.md for the full list of npm scripts.
7474. Ensure this port is available.libsecret is installed for keytar to function. On macOS/Windows, it uses the native keychain.--logout flag to clear corrupted or invalid tokens from the keychain.See CONTRIBUTING.md for guidelines on setting up the project, code style, adding new tools, writing tests, and submitting pull requests.
This project is licensed under the Apache-2.0 License.
Выполни в терминале:
claude mcp add davinci-mcp-server -- npx Web content fetching and conversion for efficient LLM usage.
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