Oracle Fusion HCM MCP Server

Oracle Fusion HCM MCP Server

An MCP server that lets AI models interact with Oracle Fusion Cloud HCM across its entire REST surface. It uses generic, schema-aware tools to dynamically discover licensed modules and resources without per-endpoint code.

Category
Visit Server

README

Oracle Fusion HCM MCP Server

An MCP server that lets AI models interact with Oracle Fusion Cloud HCM across its entire REST surface — workers, org structures, compensation, absence, payroll, recruiting, talent, learning and more — without hand-coding a tool per endpoint.

Built to be packaged once and reused across many Fusion HCM customers, regardless of which modules they license, how their flexfields are configured, or which Oracle release they run.

⚠️ Status: Early development. The technical design is complete (DESIGN.md). Phase 1 read core is implemented — discovery (list_resources, describe_resource, get_capabilities), generic read (query_resource, get_record), q= filter validation, and the safety layer (PII redaction + audit log). Pending validation against a live pod. Writes/ATOM/BIP follow in later phases.


Why this exists

Oracle Fusion HCM exposes ~600 REST resources built on Oracle's uniform ADF REST framework. A naive MCP server would create one tool per resource and blow up the model's context window — and would advertise tools that don't exist for customers who haven't licensed the matching module.

This server takes the opposite approach: a small set of generic, schema-aware tools that introspect any resource at runtime via Oracle's /describe endpoint, plus a handful of curated workflow tools for the most common HR tasks. It discovers each customer's licensed footprint automatically and only exposes what actually works on their pod.

Key design principles

  • Generic over hardcoded — 6 generic tools + ~15 curated workflows cover all ~600 resources. No per-resource code.
  • Self-documenting — the model reads live schemas via describe_resource; no bundled schema files to maintain.
  • Capability-aware — startup probing detects which Oracle modules are licensed/provisioned and lights up only those tool groups (no Recruiting license → no Recruiting tools).
  • Safe by default — read-only out of the box; writes are off by default, gated, dry-run-first, and audited. PII (national IDs, salary, DOB) is redacted unless explicitly enabled.
  • Distributable — one configurable artifact, deployed single-tenant per customer pod. No customer specifics in code.

Architecture at a glance

auth/     Basic + OAuth2/JWT (OCI IAM / IDCS)
core/     ADF REST client · resource catalog · /describe cache · q= filter builder
tools/    discovery · query · workflows · mutate · atom · bip
safety/   PII redaction · audit log · dry-run · confirm gates
config.py base URL · pinned REST version · scopes · feature & module flags

Tools (Phase 1 read core)

Tool Purpose
list_resources Search/enumerate the HCM resource catalog
describe_resource Return a resource's schema, children, and actions (/describe)
get_capabilities Report which modules are live on this pod
query_resource Generic GET with q/fields/expand/paging — the workhorse
get_record Fetch one record by key, optionally expanding children

Writes (mutate_record, run_action), ATOM change-feeds, and BI Publisher tools are specified in the design and arrive in later phases — all off by default.

Roadmap

Phase Deliverable
1 Generic read core + auth + discovery + safety scaffolding
2 ~15 curated HR workflow tools
3 Gated writes + custom actions + audit
4 ATOM change feeds (new hires, terminations, updates)
5 BI Publisher / HCM Extracts reporting

Stack

Python · FastMCP · httpx · pydantic Packaged as a Docker/OCI image (primary) built from a hatchling wheel.

Getting started

Configure

cp config.example.toml config.toml   # then edit; supply secrets via HCM_* env vars

Required: server.base_url (or HCM_BASE_URL). Credentials should come from environment variables (HCM_USERNAME/HCM_PASSWORD, or HCM_CLIENT_ID/HCM_CLIENT_SECRET/HCM_TOKEN_URL), never the committed file. See config.example.toml.

Run with Docker (primary)

docker build -t aj-fusion-hcm-mcp .
docker run --rm -i \
  -e HCM_BASE_URL="https://your-pod.fa.ocs.oraclecloud.com" \
  -e HCM_USERNAME="INTEGRATION_USER" -e HCM_PASSWORD="..." \
  -v "$PWD/config.toml:/app/config.toml:ro" \
  aj-fusion-hcm-mcp

For hosted HTTP transport, set transport.type = "http" (or HCM_TRANSPORT=http) and publish -p 8000:8000.

Local development

python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
pytest
aj-fusion-hcm-mcp          # runs the MCP server over stdio

Documentation

  • DESIGN.md — full technical design: auth, the ADF REST client, exact tool signatures, the q= filter grammar, the safety model, and licensing/module alignment.

Status & contributions

This is an actively developing project. The design doc is the source of truth; see open questions in DESIGN.md §10.

License

TBD.

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured