Temporal MCP. Debug durable workflows right from your chat.
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Temporal MCP Server monitors and manages distributed workflows in Temporal Cloud. Your AI client can list active runs, search for failed executions using visibility syntax, or retrieve the entire event history to debug complex processes instantly.
What your AI agents can do
Get namespace details
Retrieves core metadata, including the configured retention period, for your current namespace.
Get workflow details
Fetches high-level status and configuration details for a single workflow execution ID.
Get workflow history
Retrieves the full, detailed event history log for any specific workflow run to track its entire lifecycle.
List all workflow runs, filtering by status (running, completed, failed) using list_workflows.
Pull detailed information for a single workflow execution with get_workflow_details.
Get the full chronological sequence of events, activities, and signals using get_workflow_history.
Run advanced queries (like SQL) across your runs using Temporal Visibility syntax via search_workflows.
Check the configuration, retention periods, and metadata of your operational namespace with get_namespace_details.
List all recurring workflows using list_schedules and predict their next run times.
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Supported MCP Clients
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Temporal MCP Server: 7 Tools for Durable Workflows
These tools let you manage every aspect of your distributed system's execution state—from listing basic statuses to running deep, query-based historical audits.
019d7611get namespace details
Retrieves core metadata, including the configured retention period, for your current namespace.
019d7611get workflow details
Fetches high-level status and configuration details for a single workflow execution ID.
019d7611get workflow history
Retrieves the full, detailed event history log for any specific workflow run to track its entire lifecycle.
019d7611list schedules
Returns a list of all defined recurring workflows and when they are scheduled to run next.
019d7611list search attributes
Shows you what custom attributes are available for searching within the namespace, helping structure your queries.
019d7611list workflows
Lists all workflow executions in the configured namespace, providing their IDs and current status (running, failed, etc.).
019d7611search workflows
Allows running complex queries across runs using Temporal Visibility syntax to filter results by type or status.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
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- Use this MCP plus 4,700+ others, all in one place
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- Works with Claude, ChatGPT, Cursor, and more
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What you can do with this MCP connector
This MCP Server connects your AI client directly to Temporal Cloud's execution engine. It lets you treat complex workflow management data—the kind SREs use every day—like simple API calls right from your chat window. You don't gotta open the clunky UI or run command line tools; your agent handles it all.
Understanding Your Workflow Environment
You can check the operational state of your system first. The get_namespace_details tool pulls core metadata for your current namespace, letting you verify things like the configured retention period and overall settings. Before running any searches, use list_search_attributes to see exactly what custom attributes are available in the namespace; this helps structure your queries correctly.
Monitoring Runs and Status
Need a quick status check? The list_workflows tool gives you an immediate roster of all workflow executions in the configured namespace, listing their IDs and current status—whether they're running, completed, or failed. If you're tracking scheduled jobs, list_schedules returns a list of every defined recurring workflow, showing when each one is slated to run next.
To get specific details on any single run, use get_workflow_details; this fetches the high-level status and configuration data for just that one workflow execution ID.
Advanced Search and Querying
When you gotta dig deep, the system provides advanced querying options. The search_workflows tool lets you run complex queries across your entire history using Temporal Visibility syntax. You can filter results by specific types or statuses to narrow down huge datasets fast. If you're not sure what parameters work for a search, check out list_search_attributes first.
Debugging Failures and Tracking History
Debugging is where this thing shines. When a workflow fails, the single most valuable thing is the full sequence of events. The get_workflow_history tool retrieves the complete event history log for any specific run ID; you'll see every activity, signal, and change in state that happened during its entire lifecycle, letting you pinpoint exactly where things went wrong.
For even broader visibility than a single failure, you can use search_workflows to find groups of failed runs or successful batches. The combination of tools means your agent handles the full audit trail: check namespace settings with get_namespace_details, list all running jobs using list_workflows, and pull the entire chronological event record for deep debugging with get_workflow_history.
It's built for platform engineers who need answers, period.
How Temporal MCP Works
- 1 Subscribe to the server, then enter your Temporal Address, Namespace, and API Key (or cert path).
- 2 Your AI client executes a command—for instance, 'Show me all failed payment workflows.'
- 3 The agent uses the appropriate tool (
search_workflows) to query the cluster state and returns the actionable data set.
The bottom line is: you talk to your AI agent like it's a dashboard, but the server does the heavy lifting by connecting directly to Temporal's core API.
Who Is Temporal MCP For?
This is for Platform Engineers and Site Reliability Engineers who are tired of clicking through multiple dashboards just to figure out why a multi-step job failed. If you spend your mornings debugging complex, long-running processes that fail unpredictably, this server saves you hours.
Debugging failed workflow runs and retrieving precise event histories without having to open the Temporal UI.
Querying complex visibility logs during an incident response or auditing namespaces for consistency across multiple services.
Listing and checking retention periods on operational namespaces to ensure data compliance and resource management.
What Changes When You Connect
- Instant debugging: Instead of jumping into the Temporal UI, you can ask the agent to run
get_workflow_historyand immediately see every event that happened during a failure. That saves time. - Targeted searching: Need to find all runs that failed last week? Use
search_workflowswith specific criteria (e.g.,Status="Failed") instead of manually filtering huge lists. - Know your limits: Run
get_namespace_detailsanytime to check the retention period or configuration metadata, making sure you stay compliant without logging into a separate console. - Manage recurring jobs: Use
list_schedulesto get a clean overview of every scheduled workflow and see exactly when the next critical job (like a nightly backup) is due. - Simple status checks: The
list_workflowstool gives you a quick rundown of all active runs, letting you know if anything unusual—like an unexpected spike in failures—is happening right now.
Real-World Use Cases
The Failed Payment Run
A customer reports their payment failed. The agent uses search_workflows to filter for 'Payment' workflows with a 'Failed' status. Then, it runs get_workflow_history on the resulting ID, pinpointing that the failure occurred specifically at the 'ChargeCreditCard' activity. Problem solved.
Auditing Namespace Drift
Platform Ops needs to ensure all development namespaces follow the same data retention rules. They call get_namespace_details for multiple namespaces, quickly confirming that every one has a 30-day limit set correctly. No manual checks needed.
Checking Next Maintenance Window
The team needs to know when the nightly database backup runs. They ask the agent to run list_schedules. The server immediately returns the scheduled ID and calculates that the next execution starts in 4 hours, allowing them to prepare.
Diagnosing a Stalled Pipeline
The data pipeline workflow hasn't updated. An engineer uses list_workflows to check status, finds an old run is 'Running', and then calls get_workflow_details. The details show the process timed out, allowing them to restart it immediately.
The Tradeoffs
Checking every failure manually
A developer opens the Temporal UI, clicks 'Failed,' scrolls through 50 runs, and copies a few Run IDs to debug them one by one.
→
Instead, ask your agent to search_workflows using criteria like Status="Failed" AND StartTime > "yesterday". This filters everything instantly so you only get the relevant failures.
Guessing namespace configs
Assuming a new team's namespace follows standard retention rules, potentially leading to data compliance violations.
→
Always run get_namespace_details. This confirms the current configured metadata and retention period before you write any code that relies on those boundaries.
Sequential lookups for status
Calling list_workflows to get all IDs, then calling get_workflow_details for each ID sequentially.
→
Use targeted tools. If you know the ID, use get_workflow_details. If you need a list, start with list_workflows, but if you need filtered results, jump straight to search_workflows.
When It Fits, When It Doesn't
Use this server if your primary job involves debugging long-running, mission-critical processes (like payments or data ETL) and you need visibility into the historical execution path. You're looking for the 'why' when things break.
Don't use it if all you need is a simple database query on static records—that's where standard SQL tools belong. Also, don't use it if your state management is handled by another service entirely; this tool works only with Temporal Cloud data structures. If the process failure isn't tracked as an 'Activity' or 'Signal' within the workflow execution log, you won't see it here.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Temporal. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 7 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Debugging complex processes shouldn't require a dozen dashboard clicks.
Right now, when something breaks in your distributed system, you open the Temporal UI. You select the namespace, filter by 'Failed,' scroll through dozens of runs, and then for each run ID, you have to click another tab—the history view—just to see if it timed out or crashed on a specific activity.
With this MCP server, your agent handles all that clicking. You just ask: 'What went wrong with the payment workflow?' The server executes `search_workflows` and then automatically provides the full event log using `get_workflow_history`. You get the answer in plain text, without leaving your chat window.
Temporal MCP Server: Get execution history via agent.
You eliminate the need to remember which dashboard holds which piece of metadata. No more toggling between 'Namespaces' view, then running a separate query for 'Schedules,' and finally opening the run details in another tab just to check retention policies.
Everything is available through natural conversation. It turns complex operational knowledge into simple, callable functions. You can audit everything—from `list_workflows` status checks to deep dives with `get_workflow_history`—without ever touching the UI.
Common Questions About Temporal MCP
How do I find a specific failed workflow using get_workflow_details? +
You first need the Run ID. Use list_workflows or search_workflows to find the correct Run ID, and then pass that ID to get_workflow_details. This gives you the high-level status before digging into history.
Can I use search_workflows for anything other than failures? +
Yes. You can filter by any attribute Temporal tracks, like workflow type or start time. You must use the full Visibility Query syntax, which is what makes search_workflows powerful.
Does get_namespace_details tell me about individual workflows? +
No. It only provides metadata about the namespace itself—stuff like its retention policy and configuration settings. Use list_workflows for actual workflow data.
What's the difference between list_workflows and search_workflows? +
Simple: list_workflows gives you a raw, basic list of IDs and statuses in the namespace. search_workflows lets you apply complex filters (like finding all 'Payment' workflows that failed last week).
How do I check if my scheduled job is running? +
First, run list_schedules to see the schedule name. Then, use get_workflow_details on a recent execution ID associated with that schedule to confirm its current operational status.
Before running a complex query with `search_workflows`, how can I find out which attributes are available? +
Run list_search_attributes first. This tool returns every custom attribute defined in your namespace. Knowing these fields ensures your search syntax is correct and prevents failed queries.
What specific configuration details does `get_namespace_details` reveal about the cluster? +
It provides essential metadata, including the configured retention periods for logs and the current operational status of the namespace. This helps you audit consistency across your distributed systems.
When I use `get_workflow_history`, what does the returned data structure look like? +
The history returns a chronological sequence of events, including timestamps, activity names, and signal payloads. It doesn't just show status; it shows every step taken during execution.
Can my AI agent debug a failed Temporal workflow run directly? +
Yes. Instead of browsing the Temporal UI to manually dissect the execution, feed the workflow ID directly to your AI agent. It can seamlessly fetch the entire event history, identify the exact panic line or activity timeout, and immediately suggest code fixes within your IDE.
How complex can the visibility searches be via natural language? +
Very complex. Because your agent understands your codebase, you can ask 'Find all workflows of type ProcessPayment that failed in the last 2 hours'. The agent formulates the perfect SQL-like syntax for the backend, parses the matching pages, and surfaces exactly which transactions failed.
Can it read scheduled jobs and Cron jobs configurations inside Temporal? +
Absolutely. You can request a list of all active schedules mapped to your namespace. Your agent unpacks JSON configurations, showing definitions of workflow recurrence, enabling you to detect missing crons without interrupting your workflow.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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