Coalesce MCP. Manage Snowflake data pipelines via conversation.
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Coalesce manages Snowflake data pipelines through your AI agent. Use this MCP to list all environments, check job statuses, and trigger complex transformations without ever opening the Coalesce UI.
It lets you run critical ETL jobs and debug pipeline failures using natural conversation.
What your AI agents can do
Get environment
Pulls detailed configuration information for a single specified data environment.
Get job details
Retrieves comprehensive details about a specific, known job within the system.
Get run status
Checks and reports on the current progress or final status of an ongoing data run.
Retrieves a full list of every configured data environment in your organization.
Determines the current progress, success state, or failure details for any running pipeline job.
Starts a new data transformation job in a specific environment on demand.
Ask AI about this MCP
Supported MCP Clients
OAuth 2.0 Compatible Gemini Waiting for input…
Coalesce: 8 Tools for Data Pipeline Control
These eight tools let you control every aspect of your data transformation process—from listing all environments to triggering specific jobs and checking run statuses.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Coalesce on Vinkius019d7575get environment
Pulls detailed configuration information for a single specified data environment.
019d7575get job details
Retrieves comprehensive details about a specific, known job within the system.
019d7575get run status
Checks and reports on the current progress or final status of an ongoing data run.
019d7575list environments
Gets a complete list of every available environment configured in your Coalesce account.
019d7575list jobs
Retrieves a catalog of all jobs, allowing you to narrow the search by environment or other criteria.
019d7575list nodes
Gets metadata about individual transformation nodes within a specific data environment for inspection.
019d7575trigger job
Starts a specified data job in an environment, initiating the full pipeline run.
019d7575trigger run
Initiates a new, general pipeline run for an entire environment or a specific job.
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
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
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- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Coalesce, then connect any of our 4,800+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,800+ others, all in one place
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- Works with Claude, ChatGPT, Cursor, and more
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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Coalesce. 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 8 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Manual Data Pipeline Oversight
Today, checking on critical production pipelines means jumping between three different tabs: the main dashboard, the job history log, and the environment configuration page. You copy a run ID from one place and paste it into another to check its status, then manually search through logs for that specific failure code. It takes fifteen minutes of clicks just to get an answer.
With this MCP, you ask your agent, 'What happened with yesterday's data load?' The system handles the whole sequence: checking environment health first, finding the correct job ID, and reporting back the exact error details in a single response. You get immediate answers without leaving the chat window.
Coalesce MCP: Full Pipeline Control
You eliminate the need to manually copy run IDs, navigate deep into UI logs, or remember which environment name corresponds to 'Staging'. The agent orchestrates these steps for you.
Your work shifts from clicking through dashboards to defining outcomes. You tell your agent what needs to happen—'Run X in Y environment'—and it makes it happen. Simple.
What you can do with this MCP connector
This connector gives your AI client direct control over data transformation workflows in Snowflake. You can ask it to manage pipelines by listing all environments or checking if a specific job finished successfully. Need to kick off a new batch? Your agent triggers the run, tracks its progress, and tells you when it completes.
When you connect this MCP through Vinkius, your AI client gets full visibility into every single step: which jobs were called, what data flowed through, and how long each task took. You don't have to guess what happened in the backend because Vinkius AI Analytics gives you a clear audit trail of everything that runs.
It’s all managed by your agent, keeping the process visible and reliable.
This is ideal for engineers who need hands-on control over production data—from triggering necessary transformations on demand to getting deep status reports on failed jobs.
019d7575-7acf-730a-aeac-50fd1ce74166 
How Coalesce MCP Works
- 1 Add the Coalesce integration to your AI toolset and provide the API token.
- 2 Ask your agent what you need—for instance, 'List all environments' or 'Check the status of production'.
- 3 Your agent executes the command through the MCP, pulls the real-time data back, and reports the outcome directly in chat.
The bottom line is: your AI client treats complex pipeline management like a simple conversation.
Who Is Coalesce MCP For?
Data Engineers who are tired of logging into multiple dashboards just to check if yesterday's nightly ETL run failed. Analytics Engineers who need immediate status updates on transformations without waiting for a data team member. Data Team Leads who need an instant, high-level view of environment health.
Triggers specific pipelines and monitors them from chat instead of writing complex scripts or clicking through the UI.
Quickly diagnoses why a metric is missing by checking job details and run statuses for specific data sets.
Gets an immediate overview of which environments are healthy and which ones need investigation, all in one prompt.
What Changes When You Connect
- When you need to know if the 'Production' pipeline is running, use
get_run_status. You get an immediate percentage and status update without navigating dashboards. - Instead of guessing which job needs a fix, call
list_jobsfirst. This gives you a full directory of every possible transformation, so you can target your investigation precisely. - Need to test a change? Use
trigger_jobto start the specific pipeline run immediately. The agent reports back when it's done or if it hits an error. -
list_environmentsprovides instant visibility into all available environments (Dev, Staging, Prod). You can check their basic health status with one prompt. - Debugging a complex failure? Use
get_job_detailsafter finding the job vialist_jobs. This pulls deep technical info you need to start fixing things. - Don't waste time on manual checks. With Coalesce, your agent handles the entire lifecycle—from listing environments with
list_environmentsto triggering a run withtrigger_run.
Real-World Use Cases
Nightly pipeline check failed.
The data team lead asks, 'What's the status of yesterday's load?' The agent uses get_run_status, confirms the run ID, and reports that the process stalled at 60% due to a node failure.
Need to test staging data.
A developer asks, 'Run the marketing pipeline in Staging.' The agent uses trigger_job, waits for confirmation, and reports the new run ID so they can monitor it independently.
Discovering available pipelines.
A new analyst asks, 'What transformations are available?' The agent calls list_jobs to provide a full list of all possible data sources and transformation endpoints.
Checking environment readiness.
The DevOps engineer asks, 'Are Dev and Staging environments active?' The agent uses list_environments, confirming the IDs and last known health status for both targets.
The Tradeoffs
Assuming a job exists.
A user tries to check the status by asking, 'Check my pipeline.' The agent fails because it doesn't know which run ID or environment you mean.
→
Always start broad: First, use list_environments to narrow down the scope. Then, use list_jobs to find the right job name before calling get_run_status.
Triggering a run without context.
Calling trigger_run and getting an error because no target environment or job was specified in the prompt.
→
Be specific. Specify both the environment (e.g., 'Production') and, if applicable, the job name when calling trigger_job. This keeps the run focused.
Over-relying on one tool.
Only using get_environment to check settings, but failing because you don't know which transformation node needs updating.
→
After checking environments, use list_nodes to inspect the metadata of specific nodes. This gives you the granular view necessary for proper debugging.
When It Fits, When It Doesn't
Use this MCP if your core need is managing complex data pipelines in Snowflake and you require visibility into the execution lifecycle—listing environments, checking job status, or kicking off a run. Don't use it if all you need is to query simple metrics or pull static reports; those tasks are better handled by direct database connection tools. If you just need basic API access without pipeline control, this is overkill. But if your workflow depends on knowing when and how a data transformation completed, this MCP is the right fit.
Common Questions About Coalesce MCP
How do I get my Coalesce API token? +
In the Coalesce UI, go to Organization Settings > API Tokens and generate a new token. Copy it and paste it below.
Does this work with Snowflake? +
Yes. Coalesce is built specifically for Snowflake. The API triggers jobs that run directly on your Snowflake instance.
What is a Node Selector? +
A Node Selector lets you filter which transformation nodes to include in a run, based on name, type, or tags.
When I need to troubleshoot a failure, does using the `get_job_details` tool provide enough information about what went wrong? +
Yes, it pulls detailed execution logs for you. This information lets your agent inspect the exact point of failure and often indicates whether the issue is data-related or configuration-based.
Does running `list_environments` show me all possible environments configured in my Coalesce account? +
It lists every environment set up in your organization. This means you can see not just the 'Production' instance, but also any staging or development environments for review.
If I need to build a multi-step process, can my agent chain calls together like `list_environments` followed by `trigger_job`? +
Absolutely. Your AI client handles the workflow logic between tools. You can connect them in sequence so that one tool's output feeds directly into the next tool's input.
After I call `trigger_run`, how quickly do I get an estimate of when the data transformation job will complete? +
The response from trigger_run includes a status update and an estimated completion window. This gives you immediate visibility into whether the process is running smoothly or if it might stall.
What specific metadata does the `list_nodes` tool provide for transformation nodes within an environment? +
It retrieves technical details about every node in a given pipeline. This includes unique identifiers and structural information, which is essential for deep debugging or advanced automation.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.