How to build an MCP server for Infor SyteLine
Short answer
An MCP server for SyteLine is a small service that exposes a few named tools, such as item lookup or order status, backed by IDO loads or parameterized queries on a read-only replica. It authenticates to SyteLine as a limited user, logs each call, and works with Claude, ChatGPT, and other MCP clients.
The Model Context Protocol gives AI clients a standard way to discover and call tools. For SyteLine, that means you write the connector once instead of building separate integrations for each assistant or agent framework that your teams want to try.
The hard part is not the protocol; the SDKs make a working server a few hours of code. The hard part is deciding which tools to expose, how to return SyteLine data in a form a model can use, and how to keep permissions tight on a system that runs payroll-adjacent costing and customer pricing.
This page assumes you can write Python, TypeScript, or C# and have a SyteLine analyst who knows the data. The patterns below are intentionally conservative.
What teams use it for
Planner Q&A over live inventory and supply
Tools for item availability by warehouse and open supply let a planner ask where an item is allocated without opening several forms.
Sales desk order lookups
A get_customer_order tool returns lines, promised dates, and ship status for use in any MCP client the sales team already uses.
Engineering BOM and routing inspection
Tools return BOM structure and routing operations for an item and revision, so an engineer can ask what changed or where a component is used.
Purchasing visibility
A get_po_status tool returns open lines, due dates, and receipts for a vendor, ready for an agent to rank by urgency.
Shared backend for several assistants
One audited server feeds a desktop assistant, an internal chatbot, and a scheduled agent, so permission and logging logic lives in one place.
Infor SyteLine (CloudSuite Industrial) integration surface
- IDO REST/web service on the Mongoose application server: Load Collection with a property list, filter, order by, and record cap is the primary read primitive. On Infor OS tenants, call it through the ION API gateway with OAuth 2.0.
- Custom IDO methods (.NET) as a stable contract: wrap complex logic in one method so the MCP tool calls it and your server is insulated from schema changes.
- SQL Server read-only replica or reporting login for aggregates. Core tables follow the _mst naming pattern (for example item_mst, itemwhse_mst, co_mst, coitem_mst, po_mst, poitem_mst, job_mst, jobmatl_mst) and always include site_ref. Table and column details vary by version and customization.
- User-defined fields (UETs and user fields) that add custom columns at each customer; your server needs a configuration layer to include or exclude them.
- Session and licensing: IDO calls consume application sessions or user licenses depending on your agreement, so use a pooled service user and confirm the license implications with Infor.
How it works
- 1
List tools from real questions
Interview planners, buyers, and CSRs and write the twenty questions they ask most. Group them into five to eight tools with clear names.
- 2
Choose the language and SDK
Use an official MCP SDK in TypeScript or Python, or C# if your team already builds IDO extensions. Keep the server stateless.
- 3
Implement read tools through IDOs first
Each tool validates inputs, calls an IDO load with explicit properties and a row cap, and returns compact JSON with record keys such as order and line numbers.
- 4
Use SQL only for aggregates
Where IDOs are inefficient, run parameterized, reviewed queries on the replica, always filtering by site_ref. Never let the model supply SQL text.
- 5
Authenticate and scope
Terminate OAuth or API-key auth at the server, map the caller to a role, and map the role to allowed sites, customers, and fields. Use a limited SyteLine service account behind it.
- 6
Choose a transport
stdio suits a single desktop client during development. For teams, run an HTTP transport behind your reverse proxy with TLS and authentication.
- 7
Add audit and tests
Log tool name, caller, parameters, and result size. Add contract tests that call each tool against a test company and compare to known SyteLine values.
Example flows
Item availability tool call
Trigger: A client calls get_item_availability with item and warehouse.
- Server validates the item code and caller's allowed sites.
- It loads on-hand, allocated, and on-order quantities through the relevant IDOs.
- It returns quantities with the units of measure and a timestamp.
Result: The client explains availability in plain language and cites the numbers returned.
Late order discovery
Trigger: An agent calls list_late_order_lines with a customer filter.
- Server queries open order lines past promise date for allowed customers.
- It joins the demand to open jobs or supply through a reviewed query.
- It returns up to fifty rows sorted by days late with a truncated flag if more exist.
Result: The agent can prioritize outreach and tell the user when the list was capped.
Keeping data on-prem
An MCP server is naturally on-prem friendly: it is a small service that sits next to the SyteLine application server and the SQL replica. Run it on a hardened VM inside the plant network with no inbound internet path. The client can be a desktop assistant on the LAN, an internal chatbot, or an agent runtime that uses a local open-weight model on your GPUs through vLLM or Ollama, so no ERP data crosses the boundary. If you do use a cloud model client, the server decides what is returned, which is why field filtering and row caps live on the server and not in the prompt. Plan for patching, certificate renewal, and secrets management like any other internal integration service.
Security checklist
- Expose named read tools only; do not offer generic query or IDO-call tools.
- Use a least-privilege SyteLine service user and validate every parameter before it reaches an IDO or query.
- Authenticate clients, map them to roles, and log tool calls with caller identity.
- Cap rows and fields per response and mask sensitive values such as cost or personal data by role.
- Treat tool output as untrusted when it contains free text, since notes fields can include prompt-injection text.
Ways to get it done
Build in-house with an MCP SDK and IDO calls
Effort: One to three weeks for a first set of read tools, longer with security review.
Best when you have a developer and a SyteLine analyst and want full control.
Use a marketplace or third-party connector if available
Effort: Days to weeks, mostly evaluation.
Reasonable if it is maintained, supports your version, and passes your security review. Availability varies, so verify it before planning around it.
Partner build with Netray (ERPray connectors)
Effort: Scoped pilot over a few weeks.
When you want a tested SyteLine tool set plus on-prem models and a functional expert.
Pitfalls
- !Returning whole IDO rows with every property; large payloads confuse models and leak fields.
- !Missing site_ref filters in multi-site companies, which blends plants or returns duplicates.
- !Hard-coding custom field names that differ per customer; make them configurable.
- !Using an administrator account for convenience, then discovering the audit trail shows one user for everything.
- !Neglecting versioning: renaming a tool or output field silently breaks agents that depend on it.
Frequently asked questions
Which transport should I use, stdio or HTTP?
Use stdio for local development and single-user desktop clients where the server runs on the same machine. Use an HTTP-based transport for a shared server that multiple users or agents reach over the network, with TLS and authentication in front of it.
Should the MCP server call IDOs or the database?
Prefer IDOs for transactional reads and any write, because they respect SyteLine logic and authorization. Use a read-only database replica for aggregates and reports where IDO loads would be slow, and keep those queries fixed and reviewed.
Can the same server work with Claude and ChatGPT?
Yes if the client supports MCP, which is the point of the standard. Client support and setup differ by product and plan, so confirm how each registers a remote or local server, and test authentication with each.
How many tools should I start with?
Start with five to eight read-only tools covering your most common questions. A small, well-described set is easier for a model to use correctly than a large catalogue, and easier to secure and test.
How do I keep the model from changing data?
Do not expose write tools at first. When you add them, make them create drafts or requests, require explicit approval in the client or a separate workflow, and run them under a limited account so SyteLine validation and logging still apply.
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