MCP Server (onX)

This fulfillmenttools MCP Server provides a standardized interface for interacting with fulfillmenttools. Built on the Model Context Protocol (MCP) and implementing the Order Network eXchange (onX) Protocol, it enables system-to-system communication that allows AI Agents, orchestration layers, and external platforms to manage orders using natural language without relying on proprietary APIs.

Designed for agentic commerce applications, this fulfillmenttools MCP Server exposes a set of tools that AI agents can invoke to query inventory, products, and variants — either on user instruction or as part of automated workflows. This initial release focuses on read-only operations to deliver value early without exposing security-sensitive data. Additional tools for order management may follow in future releases.

Example interactions:

• What's the current stock level for SKU-12345?

• Show me all products in the electronics category

• List available variants for product PRD-001

• Check inventory availability across all facilities

The fulfillmenttools MCP Server acts as a protocol adapter. It doesn't implement business logic, doesn't infer missing data, and transparently passes requests and responses between the AI Agent and the fulfillmenttools backend.

Using the fulfillmenttools MCP Server with an agent

The fulfillmenttools MCP Server allows any compatible agent (for example, Claude Desktop/Claude Code) to securely access and interact with onX functionality in a standardized way.

To use this feature, you first need to make the fulfillmenttools MCP Server known to the agent you plan to work with.

The following example describes the general setup using the fulfillmenttools MCP server, as demonstrated with a Claude agent. The same steps apply to other agents that support MCP integration.

1

Agent connection

Start by configuring your agent to recognize the fulfillmenttools MCP Server endpoint.

This typically involves adding the fulfillmenttools MCP server URL and name (for example, ocff-myproject.mcp.api.fulfillmenttools.com/onx, replace myproject with your fulfillmenttools projectId) to the agent's MCP or tools configuration.

Once configured, the fulfillmenttools MCP Server is available, and your agent is aware that it can call onX capabilities via the fulfillmenttools MCP Server. You need to connect to it to complete this step.

2

Authorization

You'll either need to configure a token for your agent or log in on your first interaction with the service by providing your credentials in a fulfillmenttools login form. See the Authentication section below for more information.

Successful authorization ensures the agent can securely make requests to the fulfillmenttools MCP Server on your behalf.

3

Interaction

After configuration and authorization are complete, the agent can start interacting with onX through the fulfillmenttools MCP Server. When you ask the agent to perform an onX-related task, the agent uses the fulfillmenttools MCP Server in the background of the conversation to retrieve data or trigger actions, returning the results directly in its response.

Intended usage and primary user audience

Target audience

Primary use cases

• Inventory queries: Check stock levels and availability across facilities

• Product catalog access: Retrieve product information and metadata

• Variant lookup: Query product variants and their attributes

• Agentic workflows: Enable AI agents to access fulfillment data for automated decision-making

Future target audiences

The following audiences will be supported as additional tools are implemented in the future:

User interaction model

Authentication

All requests to the fulfillmenttools MCP Server require proper authentication. Because the agent is not an independent actor in this MCP Service, the OAuth 2.1 Authorization Code Flow with PKCE is enforced, and any implicit and ROPC flows are disallowed. The agent is intended to work in the name of a user, not on its own.

1. Agent-provided authentication: The AI agent includes an authorization header with each request. This is typical for pre-configured integrations where the agent has been provisioned with valid credentials or tokens.

2. Interactive login: If no authorization header is present, you're redirected to a login page to enter your fulfillmenttools credentials. Upon successful authentication, a token is generated, and the agent uses this token for all subsequent requests within the session.

In both cases, authentication is handled transparently at the fulfillmenttools level.

Permissions

Access to this service requires the user to have the following permissions:

More information can be found in the Users, roles, and permissions article.

Allowed and expected data

The fulfillmenttools MCP Server accepts structured data through its standardized tools:

Query tools (read operations)

All inputs must conform to the onX protocol schema. fulfillmenttools validates requests against this schema before forwarding them to the backend.

Discouraged or prohibited data inputs

The following data types shouldn't be included in natural language queries or tool parameters:

System behavior

The fulfillmenttools MCP Server:

• Won't infer or auto-fill missing required fields

• Won't generate placeholder data for incomplete requests

• Won't attempt to correct malformed data

• Won't bypass backend validation rules

If required information is missing, the backend error returns a transparent error, and the AI Agent will request the missing details from the user.

Data flow

Data flow steps:

1. User request: User issues a natural language request to the AI Agent

2. AI interpretation: The AI parses the intent and invokes the appropriate MCP tool with structured parameters

3. Protocol translation: The fulfillmenttools MCP Server validates the request to the onX specifications and translates it to the fulfillmenttools API format

4. Backend execution: fulfillmenttools processes the request against the production data

5. Response return: Results flow back through the fulfillmenttools MCP Server to the AI Agent

6. Human-readable output: The AI presents the information in natural language to the user

Data processing locations and third parties

The fulfillmenttools MCP Server is deployed and operated exclusively by fulfillmenttools. It runs in the same geographical region as the customer's fulfillmenttools tenant to ensure data residency compliance and optimal performance.

Processing locations:

Third-party involvement:

The fulfillmenttools MCP Server doesn't route data to additional third-party services beyond those listed below.

Privacy consideration: The AI provider processes conversation content to understand and respond to requests. Users should be mindful of what information they include in their prompts.

Output characteristics and limitations

Output description

Response characteristics:

• Structured format: All responses follow consistent JSON schemas defined by the onX Protocol specification

• Transparent errors: Backend errors are returned exactly as received, without modification or interpretation

• Data currency: Query results reflect the current state in fulfillmenttools at the time of the request

Response types:

Known limitations

fulfillmenttools reserves the right to impose usage quotas on this feature.

Planned future capabilities:

The following operations are defined in the onX specification and planned for future implementation:

• Order management (create, update, cancel, query orders)

• Fulfillment operations (mark orders as shipped)

• Return processing

• Customer profile queries

Attempting to use unimplemented operations will return a FEATURE_NOT_AVAILABLE response with guidance on alternatives.

Human-in-the-loop

The fulfillmenttools MCP Server is designed with human oversight as a foundational principle. While AI Agents can interpret requests and execute operations, fulfillmenttools ensures users remain in control of all decisions and actions.

Oversight mechanisms

• Transparent communication: All operations, including errors, are communicated clearly to the user

• No autonomous actions: fulfillmenttools doesn't perform high-impact operations without explicit user direction

When human input is required

fulfillmenttools requests clarification rather than making assumptions when:

• Required information is missing from the request

• Multiple valid interpretations of user intent exist

• The requested action would have significant business impact

• Backend validation rules reject the request

User responsibility

Users are the decision-makers in all interactions. The AI Agent accelerates work by:

• Translating natural language into precise commands

• Presenting information in accessible formats

• Maintaining context within conversations

• Suggesting relevant next steps based on results

However, users should:

• Review proposed actions before confirming execution

• Verify that AI-interpreted data matches their intent

• Report unexpected behavior to the fulfillmenttools Support team

• Maintain awareness of what data is being shared in conversations

The combination of AI assistance and human oversight ensures efficient operations while maintaining appropriate control over business-critical actions.