Integration
Connect Azure DevOps to Azure AI Search Vector Store
When Completed Build fires in Azure DevOps, your agent runs Upload Documents in Azure AI Search Vector Store. Describe the whole flow and it is built, tested on every scenario and kept running.
What the catalog holds today. Ask for anything missing and your agent reads the API documentation and adds it.
Build solutions, not workarounds.
What do you want Azure DevOps and Azure AI Search Vector Store to do together?
Ask the assistant
Connect your AI client
Give your AI client this address.
https://mcp.apiant.aiOne connection does both: ask your client to act in an app once, or have it build an automation that keeps running. Any client that takes a remote MCP server. Nothing to install. Create your free account, then sign in once in your browser.
Set it up in your client
Claude Code
Run this in the directory you want to work in.
claude mcp add --transport http -s project apiant-ai https://mcp.apiant.aiStart Claude Code there, approve the server, and sign in in the browser page it opens.
Claude Code guide (opens in a new tab)Claude, web and desktop
Open Customize, then Connectors, then Add custom connector, and paste the address. On Team and Enterprise an owner adds it first.
Setup guide (opens in a new tab)Cursor
Add the address to your MCP configuration as a server URL. Cursor signs you in in your browser.
Setup guide (opens in a new tab)Zed
Open Settings, then AI, then MCP Servers, then Add Server, then Add Remote Server.
Setup guide (opens in a new tab)Codex and the ChatGPT desktop app
Open Settings, then MCP servers, then Add server, and choose Streamable HTTP. From the terminal, Codex takes the address with its own add command and then signs you in.
Setup guide (opens in a new tab)Any other MCP client
Paste the address where the client asks for a remote MCP server URL, and name the server apiant-ai where it lets you. APIANT's skills reach the client over the same connection.
Setup guide (opens in a new tab)Example flows
What Azure DevOps can start in Azure AI Search Vector Store
When Completed Build in Azure DevOps
then Upload Documents in Azure AI Search Vector Store
Starting points drawn from the triggers and actions in the catalog today. Describe the flow you need, at any depth, and your agent builds it and tests every scenario before it ships.
Everything your agent can do with Azure DevOps and Azure AI Search Vector Store
Triggers and actions 5 triggers · 12 actions
- Code Push (Git)
Triggers when commits are pushed to a Git repository in an Azure DevOps project.
- Completed Build
Triggers when a build finishes in an Azure DevOps project, with its result (succeeded, failed, partially succeeded or canceled).
- New Work Item
Triggers when a work item is created in an Azure DevOps project.
- New Work Item Comment
Triggers when someone comments on a work item in an Azure DevOps project.
- Updated Work Item
Triggers when a work item is updated in an Azure DevOps project, with the work item's current values and the list of fields that changed.
- Add Link to Work Item
Adds a link to an Azure DevOps work item: a related, parent, child, predecessor, successor or duplicate link to another work item, or a hyperlink to a web page.
- Create Work Item
Creates a new work item (such as a Task, Bug, Issue or User Story) in an Azure DevOps project.
- Find Work Item
Finds work items in an Azure DevOps project that match a Work Item Query Language (WIQL) query, returning the matching work item IDs, or the matching links for a link query.
- Trigger Build
Queues a new run of an Azure DevOps build pipeline, optionally for a specific branch or commit and with pipeline variables or parameters.
- Update Work Item
Updates an existing Azure DevOps work item: changes its title, description, state, assignee, area, iteration, priority or tags, and can add a discussion comment. Fields left blank are not changed.
- Create Index
Creates a search index with the fields you define, including vector fields for similarity search.
- Delete Index
Deletes a search index and every document in it.
- Get Document
Returns one document from a search index by its key.
- Get Index
Returns a search index's definition: its fields, vector search and semantic settings.
- List Indexes
Returns the search indexes in your Azure AI Search service.
- Search Documents
Searches a search index by keywords, filter and/or vector similarity and returns the matching documents with their scores.
- Upload Documents
Adds documents to a search index, replacing any existing document with the same key.
No trigger or action matches that. Ask your agent to add it: it reads the API documentation and builds what you describe.
Each app on its own
Reviews
What our customers say
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Questions
Azure DevOps and Azure AI Search Vector Store, answered
What can start an automation from Azure DevOps?
Any of Azure DevOps's 5 triggers in the catalog, including Completed Build, New Work Item, New Work Item Comment and Updated Work Item. Describe the moment in plain language and your agent picks the right one.
What can your agent do in Azure AI Search Vector Store when something happens in Azure DevOps?
Any of Azure AI Search Vector Store's 7 actions, including Create Index, Upload Documents, Get Document and Get Index. Your agent maps the data both ways and tests every scenario before it ships.
Can it also run from Azure AI Search Vector Store to Azure DevOps?
Azure AI Search Vector Store has no triggers in the catalog today, so automations start in Azure DevOps. If you need the other direction, ask: your agent reads Azure AI Search Vector Store's API documentation and adds it.
Do I have to build a connector for Azure DevOps or Azure AI Search Vector Store?
No. Both are in the catalog with their triggers and actions. You describe the automation; your agent builds it, tests every scenario and keeps it running.
Can my AI client use Azure DevOps and Azure AI Search Vector Store without building an automation?
Yes. With your AI client connected to APIANT, ask it to do something in either app once and it runs that action on the spot. Ask for an automation and your agent builds it, tests every scenario and keeps it running. One connection covers both.
What happens when something fails?
Your agent works that out for you. It plans the failure paths along with the happy path, then tests every scenario before it ships.
Is there a free plan?
Yes. Every plan includes the agent, including free, and building from your own AI client spends no assistant credits.