Видимість запасів за місцезнаходженням
Пов’язуйте товари й запаси з магазинами, складами, транспортом, польовими об’єктами або точками консигнації, щоб аналізувати залишки з географічним та операційним контекстом.
A location-aware inventory view with stable item/location identity, freshness checks, pipeline-health visibility, and a deterministic API path for operational use.
The job today
Inventory review often requires people to search an ERP or WMS, compare a store or warehouse to another location, check whether the feed is current, reconcile item identifiers, and decide whether an apparent shortage is real or simply missing/stale data.
A useful first agent request
Review the authorized DataSynch activity and the supplied or approved inventory/location sample. Identify feed failures or stale activity that could make the inventory view unreliable, then summarize the inventory differences that are supported by the data. Do not adjust on-hand quantity, allocate stock, or write an inventory transaction.
Use this playbook when
- Inventory exists in ERP, WMS, store, supplier, consignment, or other feeds but teams cannot see a consistent current position by location.
- Different feeds use different item or location identifiers and the joins are unreliable.
- People are treating missing inventory as zero inventory without first checking feed freshness or integration failures.
What you need
- Inventory records with quantity, status, effective timestamp, and source-system identifiers.
- The item master and stable item/SKU/GTIN mappings required to interpret the inventory.
- The location master and stable store, DC, supplier, vehicle, or site identifiers required to place the inventory.
- The business rule for freshness: how old can a record be before the workflow should flag it as stale?
What this should produce
- A normalized inventory record tied to a stable item and location identity.
- A freshness or pipeline-health indicator so missing, stale, and true-zero conditions are not confused.
- An API/application view that can answer the agreed inventory questions by item, location, or both.
Operating playbook
- 1
Define the inventory question
Start with what the user needs to know: available quantity at a site, inventory across nearby locations, store/DC balance, consignment position, or another bounded question.
- 2
Stabilize item and location identity
Resolve item and location identifiers before joining inventory feeds. Use GTIN, SKU, GLN, partner codes, and XREF deliberately rather than assuming similarly named fields mean the same thing.
- 3
Normalize the inventory feed
Use DataSynch or an approved integration to capture quantity, status, source, timestamp, item ID, and location ID in a consistent shape.
- 4
Make freshness visible
Carry the source timestamp and define stale-data rules. Before reporting zero or unavailable inventory, determine whether the latest feed actually arrived and processed successfully.
- 5
Expose the operational query
Use the approved inventory/data APIs to answer the bounded inventory question. Add geographic context only when location, proximity, territory, or movement materially changes the answer.
- 6
Use MCP to inspect the pipeline, not invent inventory
Current MCP tools can inspect DataSynch health, failures, schema, and sample rows. Unless an authorized inventory-read tool exists, the agent should not pretend it can retrieve tenant inventory simply because the pipeline is healthy.
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
- Authorized DataSynch activity, failures, and bounded row previews exposed by current tools.
- Inventory, item, and location fields included in the supplied or explicitly authorized preview/data capability.
- Geographic context for supplied locations when approved GIS validation tools are part of the question.
What this Atlasemoji MCP tool set is not authorized to do
- The current public Atlasemoji MCP tool set is not authorized to adjust inventory, create transfers, allocate stock, release orders, or post ERP/WMS transactions.
- It does not assume that a missing or stale record means zero inventory.
Where a person stays in control
- A person decides whether a discrepancy is a count issue, data-feed problem, item/location identity problem, or operational exception.
- Any inventory correction, allocation, transfer, replenishment, or downstream business action uses an approved production workflow.
How the data stays bounded
Scope the agent to the approved company/workspace/manifest and only the inventory/location records or previews explicitly exposed for the task. Current public MCP can inspect DataSynch activity and bounded previews; it should not be described as having unrestricted live WMS or ERP inventory access.
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
- No inventory record: determine whether the business state is truly zero/absent or whether the feed is late or failed.
- Item joins fail: resolve item identity/XREF before changing quantities or location logic.
- Location joins fail: resolve the location master and external location codes before adding geographic analysis.
- The workflow requires an inventory write or allocation decision: use the approved production application/API and its authorization rules.
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 DataSynch/integration APIs to receive and normalize inventory, item, and location feeds while preserving source timestamps and identifiers.
- Use the Inventory & Items APIs/application layer for deterministic inventory reads or writes that your authorized environment exposes.
- Use geofencing, routing, or location services only when a location-aware question requires them.
Відповідні API та довідкові шляхи
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 master tables, inventory, locations, and runs. First prove the inventory feed is current; then inspect how item and location identifiers are represented before comparing quantities.
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 import and run-log workflows to prove that inventory/location data reaches the expected DataSynch surface before building discrepancy logic.
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 known-good request bodies to verify the shape of master-table or related intake data before diagnosing a quantity or identity mismatch.
Inventory & Items →
The Inventory & Items solution explains how products, parts, stock, and operational assets can be tied to locations or movement. Use it to decide which inventory context belongs in the workflow before adding integration logic.
Use this to decide what “inventory by location” means for the process—store, DC, truck, consignment, field stock, or other operational asset context.
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 when inventory data must come from or return to an ERP, WMS, spreadsheet, cloud application, webhook, or other integration surface.
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.
get_datasynch_run_statusget_failed_runssummarize_workspace_activitydetect_file_schemapreview_datasynch_rowsReview the authorized DataSynch activity and the supplied or approved inventory/location sample. Identify feed failures or stale activity that could make the inventory view unreliable, then summarize the inventory differences that are supported by the data. Do not adjust on-hand quantity, allocate stock, or write an inventory transaction.
Current MCP tools used
get_datasynch_run_statusget_failed_runssummarize_workspace_activitydetect_file_schemapreview_datasynch_rowsCheck whether inventory visibility is stale because the feed failed
Review recent DataSynch activity and failed runs for the authorized workspace that carries inventory data. Report whether the feed appears healthy, stale, failed, or incomplete and identify the evidence. Do not infer inventory quantities that are not available through an authorized tool.
Profile a new inventory feed before mapping
Inspect the representative inventory sample, detect its schema, preview normalized rows, and identify likely item ID, location ID, quantity, status, and timestamp fields. Flag ambiguous mappings for human review.