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SAP SuccessFactors

SAP SuccessFactors

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About SAP SuccessFactors

SuccessFactors Toolkit is a self-hosted toolkit for troubleshooting SAP SuccessFactors APIs, extracting payloads, and building integrations. It offers REST and MCP interfaces for Compound Employee SOAP and OData queries, with PII tokenization.

Key features

  1. 01Query Compound Employee over SOAP
  2. 02Support OData v2 and v4 queries
  3. 03Extract and filter API data
  4. 04Offer REST API and MCP interfaces
  5. 05Provide PII tokenization and export

Use cases

  1. 01Troubleshoot SuccessFactors API requests
  2. 02Extract employee data for integration work
  3. 03Let AI agents query SuccessFactors data

MCP support

The toolkit provides an MCP server exposing SuccessFactors Compound Employee and OData query capabilities to AI agents, with data handling and export support. The README recommends local Docker deployment for sensitive data.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
  "mcpServers": {
    "wudaoyou-successfactors-toolkit": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "docker.io/wudaoyou/successfactors-toolkit:v0.7.0"
      ]
    }
  }
}
Run commanddocker run -i --rm docker.io/wudaoyou/successfactors-toolkit:v0.7.0

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
ocidocker.io/wudaoyou/successfactors-toolkit:v0.7.0

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

SuccessFactors Toolkit

CI License Python

A self-hosted toolkit for SAP SuccessFactors API troubleshooting, payload extraction, and integration development, exposed as a REST API and as an MCP (Model Context Protocol) server.

APIProtocolEndpoint prefix
EC SFAPI — Compound EmployeeSOAP 1.1/api/sfapi/ce/
ODataREST (v2, v4)/api/odata/

OData v4 covers the SuccessFactors APIs published as v4 services (for example Calibration and Continuous Feedback); Employee Central and Onboarding data stay on v2. See OData API.

This is an independent project, not affiliated with or endorsed by SAP SE. SAP and SuccessFactors are trademarks of SAP SE.

Start here: Docker MCP → credentials → ask → export

Data security: prefer local deployment. For sensitive SuccessFactors employee data and credentials, we recommend running the MCP server in local Docker and using a local AI agent, rather than an online AI platform or a third-party hosted MCP service. Keep credentials and exports on your machine; do not upload private keys or employee payloads to online platforms.

A local AI agent is not necessarily a local model. If it calls a cloud model, prompts, tool responses, previews, and file contents supplied to that model may leave your machine. If HR data must stay within your controlled environment, use a locally hosted model and local file-processing tools, and check the agent's outbound data handling. Local Docker alone does not guarantee this. The toolkit still connects to your configured SuccessFactors tenant to query data.

For functional consultants and business key users, start with the business user guide or its English/Chinese HTML edition. Ask IT to complete the one-time Docker Compose setup and provide approved connection files. Then:

  1. Check Docker Desktop or your IT-managed Docker service is running, then open your local AI application.
  2. Confirm with IT that the connection files are in sf-toolkit/credentials/systems, one folder per SuccessFactors system.
  3. Confirm which SuccessFactors system to use and ask for the employee, date, and information you need.
  4. Find results under sf-toolkit/data/mcp. Ask a file-capable AI application for CSV or another supported format, or a copy in an authorized folder.

The guide includes example business questions, completion checks, troubleshooting, and expandable one-time settings for your administrator. Original OData results are JSON; Compound Employee results are XML. CSV conversion requires local file tools in the AI application.

Documentation

PageCovers
REST APIFail-closed setup, install and run, cheat sheet, response format
Connect to SuccessFactorsKey pair generation, system files (SYSTEMS_DIR/<name>/<name>.json), environment variables, system management
EC SFAPI (SOAP)Compound Employee single lookup, structured filter query, pagination, known footguns
OData APIexecute / extract / extract-by-filter-in, OData v4 services, per-request connection override, known API footguns
MCP serverTools for AI agents, payload handling, export formats, PII tokenization, plugins
DevelopmentLocal dev setup, linting, tests, release process

Data handling

Use synthetic examples and test fixtures. Credentials, certificates, tenant exports, employee payloads, and generated results do not belong in Git — .gitignore and scripts/check_repository.py are a basic guardrail, not a complete secret or personal-data scanner. See SECURITY.md for deployment guidance and how to report a vulnerability.

License

Apache License 2.0. Copyright 2026 Justin Gong. See NOTICE for third-party and migrated-code attribution.

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.