Coalesce MCP. Manage Snowflake data pipelines from chat.
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Coalesce. Manage Snowflake data pipelines by triggering transformations, monitoring job status, and listing environments using your AI agent. It connects your AI client to the Coalesce platform, letting you manage complex data workflows without touching the UI.
Check environment configs, see job runs, and start jobs on demand via natural language commands.
What your AI agents can do
Get environment
Gets detailed information for a single, named Coalesce environment.
Get job details
Retrieves specific information about a single job run.
Get run status
Checks the current status and progress of a triggered data run.
List all environments in your Coalesce organization and get detailed config for any specific environment.
Check the current status, progress percentage, and execution logs for any running or completed data job.
Start a transformation job in a specific environment using trigger_job.
Kick off a new run for an entire environment, optionally targeting a specific job within that run.
Get metadata about transformation nodes within an environment to validate pipeline structure.
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Coalesce MCP Server: 8 Tools for Data Pipeline Control
These eight tools let your AI agent interact with Coalesce. You can list environments, track job status, and start pipelines using simple natural language commands.
019d7575get environment
Gets detailed information for a single, named Coalesce environment.
019d7575get job details
Retrieves specific information about a single job run.
019d7575get run status
Checks the current status and progress of a triggered data run.
019d7575list environments
Retrieves a full list of all environments configured in your organization.
019d7575list jobs
Gets a list of available jobs, with an option to filter by environment.
019d7575list nodes
Retrieves metadata details about transformation nodes in a specific environment.
019d7575trigger job
Starts a specific transformation job run within a chosen environment.
019d7575trigger run
Starts a new run for an entire environment, optionally targeting a specific job.
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What you can do with this MCP connector
You can manage your Snowflake data pipelines using your AI client through this Coalesce MCP Server. Your agent talks directly to the Coalesce platform, letting you handle complex data workflows without ever touching the web UI. You'll see all the environments you've set up and check the details on any specific one.
You can also check the full list of jobs available, filtering them by environment if you need to. To see the nitty-gritty of a particular job run, you can get specific job details or check the overall run status and progress. If you need to see the pipeline structure, you can list the metadata for transformation nodes within an environment.
You can kick off a specific job run in a given environment using trigger_job. You can also start a new run for an entire environment, and you've got the option to point that run at a specific job. You can view a full list of every environment configured in your organization using list_environments.
To get detailed info on a single environment, you can use get_environment. To list all available jobs, you'll use list_jobs. To get the full metadata for transformation nodes, you'll use list_nodes.
If you want to start a transformation job, you'll use trigger_job. If you need to start a whole environment run, you'll use trigger_run. To check the current status of any data job, you'll use get_run_status. To retrieve specific information about a job run, you'll use get_job_details.
How Coalesce MCP Works
- 1 Add the Coalesce integration to your AI client's toolset.
- 2 Provide your API Token (from Organization Settings > API Tokens).
- 3 Use natural language to tell your agent what needs to happen (e.g., 'Check the status of the Production environment' or 'Trigger the staging job').
The bottom line is, your AI client executes complex data operations by calling the Coalesce API, letting you manage data pipelines via chat.
Who Is Coalesce MCP For?
Data Engineers, Analytics Engineers, and Data Team Leads. You're the person who gets pulled into debugging a broken pipeline at 2 AM, or who needs to validate a job run before a major release. You hate opening the Coalesce UI just to check a status or kick off a test run. This lets you manage the entire data lifecycle from your chat window.
Trigger and monitor pipeline runs without opening the Coalesce UI. They use this to validate job changes or start test runs quickly.
Check job statuses and debug failed transformations directly from a chat interface instead of clicking through dashboard logs.
Get quick overviews of environment health and recent job results to keep the team updated without needing to jump between multiple dashboards.
What Changes When You Connect
- You immediately get a full list of environments using
list_environments, so you don't have to guess which pipeline is active or where to start debugging. - Need to know if Production is still running? Use
get_run_statusto see the exact percentage complete and whether the job is stuck. It's real-time feedback, not a cached status. - Instead of opening the UI to start a test, call
trigger_jobdirectly. You can test a specific pipeline on demand without affecting the main environment. - If you need to debug a failed transformation, use
get_job_detailsto pull up the specific logs and failure points without manual navigation. - You can validate your data sources before running anything.
list_nodesgives you metadata about the transformation nodes, so you know the pipeline structure is correct. - Run a full environment test with
trigger_run. This starts a job across all necessary components, letting you validate the entire data flow in one go.
Real-World Use Cases
Validating a pre-deployment data fix
A developer needs to confirm that a fix works in the Staging environment before pushing to Production. They ask their agent: 'Check the status of the Staging environment run, then trigger the job for the new fix.' The agent uses get_run_status and then trigger_job, giving immediate confirmation and a new Run ID.
Debugging a failed daily ETL job
The daily job failed, and the team needs to know why. The user asks the agent to 'List all jobs in Production and get details for the failed run.' The agent calls list_jobs and get_job_details, providing the failure logs and the reason for the break.
Quick environment health check
A team lead needs a quick overview of all pipelines. They ask the agent to 'Show me the status of all environments.' The agent calls list_environments, giving an immediate, summarized health report for the entire organization.
Running a full environment validation sweep
The team needs to validate the entire data model after a schema change. The user asks the agent to 'Start a full run on the Development environment.' The agent uses trigger_run, initiating the comprehensive workflow check immediately.
The Tradeoffs
Assuming full visibility
Thinking you can just ask, 'What is the status?' and getting a generic 'running' message. This leaves you guessing if the job is stalled or actually progressing.
→
Always use get_run_status to get the detailed percentage and the current materialization stage. If that fails, use list_jobs to get the overall job history and then get_job_details to find the specific failure log.
Calling tools in the wrong order
First calling get_job_details without knowing the environment ID, or trying to trigger_job without specifying the target environment.
→
Start by calling list_environments to identify the correct environment ID. Then, use that ID when calling trigger_job or trigger_run to ensure the operation hits the right pipeline.
Treating jobs and runs as the same thing
Using trigger_job when you actually want the entire data model to refresh, or vice versa. This causes partial or incomplete data updates.
→
If you need the whole thing to run, use trigger_run on the environment. If you only need one specific, isolated pipeline to run, use trigger_job and specify the target job.
When It Fits, When It Doesn't
Use this if you need to manage complex, multi-stage data operations and require real-time status tracking. Specifically, if you need to initiate a job, check its progress, or audit environment configurations via chat, this is your tool. Don't use this if you only need to read static metadata, like a list of file names. For that, a simple database query tool will cut it out. If your problem is merely 'I need to know what jobs exist,' start with list_jobs before attempting any actions.
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.
Available Capabilities
Manually checking data pipelines shouldn't feel like a full-time job.
Right now, checking a data pipeline status means logging into the Coalesce UI, navigating to the correct environment, clicking the job name, and then refreshing the status dashboard repeatedly. If you need to check three environments, that's three logins, six clicks, and 15 minutes of copy-pasting IDs into a spreadsheet.
With the Coalesce MCP Server, you ask your agent: 'What's the status of Production and Staging?' It calls `list_environments` and `get_run_status` and gives you a clean, summarized report back in the chat. You get the answer without opening a single tab.
Coalesce MCP Server: Manage job runs from your agent.
No more opening the UI just to trigger a test run. Instead of manually finding the job name, clicking 'Run', and hoping it works, you just tell your agent, 'Trigger the job in Development.' The agent handles the `trigger_job` call, and the run starts immediately.
You manage the entire data lifecycle—from listing environments to triggering and monitoring runs—directly through natural conversation. It's command-line power, but conversational.
Common Questions About Coalesce MCP
How do I check the status of an existing job run using the get_run_status tool? +
Use get_run_status and provide the specific Run ID. This tool gives you the percentage complete, the current stage (e.g., 'materializing fact nodes'), and the estimated time remaining.
Can I list all environments using the list_environments tool? +
Yes, list_environments retrieves a full list of all configured environments in your Coalesce account. This is the starting point for any overview or health check.
What is the difference between trigger_job and trigger_run? +
Use trigger_job when you want to run one specific pipeline within an environment. Use trigger_run when you want the entire environment's full suite of data transformations to kick off.
How do I see the available jobs in a specific environment using the list_jobs tool? +
Call list_jobs and pass the environment name as a filter. This returns a list of all available jobs in that environment, letting you know what you can run.
Do I need an API token to use the get_environment tool? +
Yes, you must provide an API token from your Coalesce organization settings. This token authenticates your agent and gives it permission to read environment details.
When should I use the list_nodes tool to check transformation metadata? +
Use this tool when you need to see the available building blocks for a pipeline. It retrieves metadata for transformation nodes within a specific environment, letting you plan or debug complex data flows.
What does the get_job_details tool provide about a specific job? +
It gives you a full profile of a job, including its configuration, dependencies, and last run summary. This is helpful for understanding what a job is supposed to do before triggering it.
Does the trigger_run tool require me to specify a job name? +
No, the trigger_run tool doesn't require a job name. You can start a fresh run for an entire environment, which is useful when you want to run a full, general pipeline update.
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.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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