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.
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
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
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.
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.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
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.
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.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
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.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.