Lưu trữ tích hợp & chứng minh workflow
Tiếp nhận, chuẩn hóa, định tuyến và giám sát dữ liệu đối tác hoặc ứng dụng được phê duyệt qua Atlasemoji và dịch vụ Google Cloud, đồng thời giữ các translator hoặc hệ thống kinh doanh hiện có khi chúng vẫn phù hợp.
A proven integration contract covering intake, normalization, mapping, exceptions, delivery, monitoring, and the exact boundary between Atlasemoji and existing systems.
The job today
Integration work typically combines source-system owners, transport and credentials, representative payloads, mapping, translation boundaries, destination behavior, retries, monitoring, and support ownership. The risk is building too much before the data contract and system boundary have been proven.
A useful first agent request
Inspect this representative source sample for the authorized integration proof. Detect the schema, preview normalized rows, identify stable identifiers, and propose the mapping questions that should be resolved before configuration is written. Call out what remains in the source, translator/VAN/iPaaS, Atlasemoji, and destination. Do not create credentials or change a connector.
Use this playbook when
- A partner or application needs a hosted data path but the business does not need another large standalone integration product.
- Data must be received, normalized, routed, monitored, and delivered between systems using APIs, files, webhooks, cloud storage, SFTP, EDI-related flows, or other approved connectors.
- You want to prove the integration with sample data and measurable behavior before productionizing it.
What you need
- The source system, transport method, authentication model, and representative payloads/files.
- The target system or Atlasemoji destination plus the expected normalized business objects.
- Mapping rules, stable identifiers, exception expectations, and any translation boundary owned by another system.
- Operational expectations such as schedule, latency, volume, retry behavior, monitoring, and ownership.
What this should produce
- A documented end-to-end integration contract and system boundary.
- A tested normalization/mapping path with visible exceptions and sample outputs.
- A production handoff that identifies credentials, monitoring, delivery, support ownership, and rollback/escalation behavior.
Operating playbook
- 1
Draw the boundary first
Write down what the source owns, what Atlasemoji should do, what the destination owns, and where translation or business logic remains outside Atlasemoji. This prevents the integration from quietly becoming a replacement project.
- 2
Prove intake with representative data
Receive a bounded sample through the intended class of transport or a safe test equivalent. Confirm the schema, identifiers, encoding, and required business fields before adding downstream complexity.
- 3
Normalize and map to the business destination
Use DataSynch mappings and XREF where needed to transform the sample into the target operational objects such as locations, items, inventory, CRM, replenishment demand, telemetry, or another approved destination.
- 4
Design the exception path before production
Define what happens to malformed data, missing identifiers, mapping failures, delivery failures, and downstream errors. Make the exception observable and assign an owner.
- 5
Prove outbound delivery and monitoring
Verify the target receives the expected result and that run status/failures can be inspected. Measure the behavior that matters: completeness, latency, failure visibility, or another agreed acceptance criterion.
- 6
Productionize only the proven pieces
Move credentials, schedules, connector configuration, writes, and downstream actions into the approved production path. Keep MCP as an inspection/preparation interface unless additional capabilities are explicitly authorized.
Agent access for this playbook
Think of each MCP tool as a bounded API capability packaged for an agent: a defined operation, accepted inputs, approved tenant/resource scope, and returned result. The agent can reason across the tools it has been given, but the connection and underlying API authorization still determine what those tools can reach and do.
What the agent may inspect
- Representative source samples, detected schemas, bounded previews, and proposed mappings through current DataSynch preparation tools.
- Authorized DataSynch run status, failed runs, and workspace activity for diagnostic review.
- The documented integration boundary and evidence returned by approved Atlasemoji APIs/applications.
What this Atlasemoji MCP tool set is not authorized to do
- The current public Atlasemoji MCP tool set is not authorized to create connector credentials, change schedules, enable writes, promote mappings, or push configuration into production.
- It does not imply support for a connector or external system that has not actually been configured and approved in the customer environment.
Where a person stays in control
- A person approves the system boundary, mapping contract, exception ownership, credentials, delivery behavior, and acceptance criteria.
- A person or approved production process authorizes connector configuration, schedules, writes, downstream delivery, rollback, and production promotion.
How the data stays bounded
Keep MCP access limited to sample preparation, mapping review, status, failures, and activity for the approved workspace/manifest. Connector credentials, writes, schedules, outbound delivery, and production configuration belong to the authorized API/integration path.
Atlasemoji bounds agent access through the connection and the API-backed capabilities behind each tool. Issued credentials or API keys, tenant context, tool scope, resource identifiers such as workspace or manifest, permitted action, and environment all contribute to the authorization boundary. The agent works through the approved tool contract rather than receiving blanket platform access.
Read-only is the policy of the public Atlasemoji MCP tool set documented today, not a limitation of MCP itself. A customer or partner can separately approve additional API-backed tools with narrower or broader read, write, execution, or production scopes for a specific relationship.
Decision points
- Existing translator/VAN/iPaaS already performs translation well: keep it and integrate at the normalized boundary.
- Source format is unstable or poorly understood: stop and profile samples before building mapping logic.
- Failures cannot be observed or assigned: the integration is not production-ready even if the happy path works.
- A requirement needs a production write or privileged connector action: move it to the approved API/integration authorization path rather than extending a read-only MCP prompt.
Two access patterns, one Atlasemoji platform
Atlasemoji APIs are foundational in both patterns. A direct REST integration calls documented APIs from your application. An MCP integration lets an AI agent use selected Atlasemoji API and service capabilities through typed tools, with agent-oriented discovery, tool scoping, tenant/resource context, and policy controls around those calls.
Call Atlasemoji APIs directly from your code
Use the direct API path when your application already knows the exact operation it needs to perform. Your code authenticates, calls the documented endpoint, supplies the required company/workspace/manifest or object context, and receives the machine-readable result. This is ideal for repeatable, deterministic integration behavior.
Application / integration → API credential → Atlasemoji API → approved capability → resultLet an agent work through approved API-backed tools
Use MCP when the request is variable and benefits from an agent choosing among approved capabilities, comparing evidence, validating context, or explaining the next step. The agent calls MCP tools; Atlasemoji maps those tools to the underlying authorized API/service capabilities. The current public Atlasemoji tool set is intentionally read-only.
Person / AI agent → Atlasemoji MCP → approved tool → Atlasemoji API/service → approved capability → resultMCP is not a black box around the APIs
The MCP layer packages selected Atlasemoji API/service capabilities as tools an agent can discover and call. That lets Atlasemoji keep API credentials, tenant/resource boundaries, permitted actions, and audit context attached to the capability while giving the agent a simpler task-oriented interface. The tool may orchestrate one or more underlying calls, but it does not bypass the API and authorization model.
You can build both paths for the same business question and compare them. Useful measures include time to answer, manual searches removed, latency, successful-answer rate, escalation rate, cost per task, and how often a person needs to intervene. Many production designs will use both: direct APIs for deterministic automation and MCP for agent-led investigation, interpretation, and orchestration.
Direct REST/API production path
Keep the system of record where it already belongs. Atlasemoji can normalize, connect, validate, add geographic context, expose APIs, host integrations, and coordinate exceptions without forcing the entire process into one application.
- Use the DataSynch integration strategy and supported connectors for intake from approved APIs, files, SFTP/FTP, cloud storage, spreadsheets, databases, SaaS applications, webhooks, or other configured sources.
- Use DataSynch mappings, XREF, and target workflows to normalize and route business records into the approved Atlasemoji or customer-owned destination.
- Use API/webhook/outbound integration paths to deliver results to downstream systems and expose status for operations.
- Use Atlasemoji's Google Cloud-oriented deployment and integration capabilities where they fit the customer's architecture; do not move systems into Atlasemoji merely to simplify the diagram.
Các đường dẫn API và trợ giúp liên quan
These pages are not duplicates. Each one answers a different question in the build process: what route exists, how several routes work together, what a valid request looks like, or what a business capability is meant to do.
DataSynch API Explorer →
The searchable DataSynch API reference. Search by business job, route, schema, tag, object type, or app surface. Each route explains what it does, the context it requires, its related schema, the next logical route, and the full Swagger contract.
Search health, integration profiles, receiver, normalize, XREF, master tables, runs, and dispatch. Those route families let you trace an integration from readiness and intake through normalization, routing, observability, and downstream delivery.
DataSynch Workbench →
A guided build surface for seeing how DataSynch routes work together. It includes workflows for imports, XREF, geofences, telemetry, dispatch, and run logs so you can understand the sequence before wiring it into your own application.
Use guided imports, XREF, dispatch, and run logs to prove the path before writing client code or moving an existing integration.
DataSynch API Examples →
Known-good DataSynch request bodies. Use these after you know which route you need and want a concrete payload to compare with your own JSON, partner data, or integration test.
Use examples once the route family is chosen so the integration starts from a valid request shape rather than reverse-engineering payloads from error messages.
DataSynch Developer Center →
The orientation page for DataSynch developers. Use it when you are not yet sure whether you need the API Explorer, Workbench, examples, quick starts, GCP guidance, or a direct/non-GCP integration path.
Start here when you still need to choose between GCP, direct API, Workbench, examples, or another supported DataSynch integration path.
API & Integration Hub →
The API & Integration Hub is the architecture-level view of REST, OpenAPI, webhooks, Google Cloud, files, applications, EDI/EDIFACT/cXML, connectors, and partner workflows. Use it when deciding how Atlasemoji should connect to the systems you already have.
Use this to draw the system boundary: which systems remain authoritative, which data Atlasemoji normalizes, which events Atlasemoji exposes, and which downstream actions remain in customer or partner systems.
Agentic path through MCP and Atlasemoji APIs
Atlasemoji MCP is an agent-facing way to use selected Atlasemoji API and service capabilities through typed tools. The MCP server authenticates the connection, exposes the approved tool surface, and invokes the underlying capabilities with the permitted company, workspace, manifest, resource, and action context. Atlasemoji's current public tool set is read-only and non-destructive by design; that is a property of this tool set, not a limitation of MCP or APIs.
Connect this playbook to ChatGPT
Start by proving the playbook in one ChatGPT conversation. Connect Atlasemoji as a remote MCP app, verify the exact tools this playbook needs, and run the first request with authorized IDs. Remember what is happening underneath: ChatGPT calls the Atlasemoji MCP tool; Atlasemoji authorizes that tool and uses the corresponding API/service capability within the approved tenant and resource scope.
- 1
Get approved Atlasemoji MCP access
Obtain approved Atlasemoji MCP access and the company, workspace, or manifest identifiers you are authorized to use. Atlasemoji-issued credentials are part of the connection and authorization boundary; the endpoint alone does not grant tenant data access.
- 2
Open ChatGPT custom-app setup
On a supported ChatGPT web plan/workspace, enable Developer mode and open Settings → Apps → Create. Workspace administrators can also create the app from Workspace settings → Apps → Create. If those controls are not available, check the current OpenAI help page below or ask your workspace administrator.
- 3
Add the Atlasemoji remote MCP endpoint
Enter https://atlasemoji.com/api/mcp as the remote server endpoint. Atlasemoji documents Streamable HTTP transport and an Atlasemoji-issued bearer credential. Select the ChatGPT authentication option that matches the access method provided for your Atlasemoji relationship; Atlasemoji support can help if your workspace needs a different supported connection method.
- 4
Scan the tools and compare them with this playbook
Use ChatGPT's Scan Tools step, then compare the discovered tools with the list shown below. Each listed tool represents a bounded Atlasemoji capability with defined inputs and outputs; behind the MCP surface, Atlasemoji invokes the corresponding authorized API/service logic rather than giving the agent unrestricted platform access.
- 5
Run one real test in a new chat
Select the Atlasemoji draft app in a new ChatGPT conversation and paste the first test request below. Replace placeholder IDs with authorized values. Evaluate both the answer and the path used to get there: which tools were called, which tenant/resource context was used, and whether the evidence was sufficient.
- 6
Make the working pattern reusable
After the test works, make the pattern reusable if your ChatGPT workspace supports Workspace Agents. Add only the apps/tools needed for the job and choose the appropriate end-user or agent-owned connection model. You can also keep the workflow as a normal ChatGPT conversation or pair it with a direct REST integration.
https://atlasemoji.com/api/mcpTransport: Streamable HTTP. Authentication: Atlasemoji-issued bearer credential. MCP tools are backed by approved Atlasemoji API/service capabilities and remain bounded by the connection, tool scope, tenant/resource context, and permitted action.
detect_file_schemapreview_datasynch_rowssuggest_field_mappingget_datasynch_run_statusget_failed_runssummarize_workspace_activityInspect this representative source sample for the authorized integration proof. Detect the schema, preview normalized rows, identify stable identifiers, and propose the mapping questions that should be resolved before configuration is written. Call out what remains in the source, translator/VAN/iPaaS, Atlasemoji, and destination. Do not create credentials or change a connector.
Current MCP tools used
detect_file_schemapreview_datasynch_rowssuggest_field_mappingget_datasynch_run_statusget_failed_runssummarize_workspace_activityProfile a candidate integration before building it
Inspect the representative source sample. Detect its schema, preview normalized rows, identify likely stable identifiers, and propose a mapping review list for the intended target. Call out missing or ambiguous fields. Do not write configuration.
Investigate a hosted integration after a failure
Review the recent DataSynch run status and failed runs for the authorized workspace. Summarize where the integration appears to fail, whether the pattern is isolated or repeated, and what evidence the owner should inspect next. Do not retry or change the integration.