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
访问服务器

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.

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选