Sifflet MCP Server
An MCP (Model Context Protocol) server that enables data observability operations with the Sifflet platform.
Features
This project provides an MCP server enabling interactions with Sifflet API :
- Explore assets: Search for tables, views, dashboards, and other data assets. View their schema, owners, tags, and their metadata.
- Explore monitors: Discover existing monitors and generate their Monitor-as-Code YAML configurations.
- Generate new monitors from a description: turn a plain-English requirement (e.g. "alert when row count drops below 1000 on the 'orders' table") into a Monitor-as-Code YAML snippet for a given list of datasets. Requires
Editorrole on the targeted domain. - Explore incidents: List all data observability incidents detected by the Sifflet platform.
- Perform impact analysis: Start from an incident and trace the downstream assets affected.
Sample Use Cases
Here are a few scenarios where the Sifflet MCP Server can be particularly helpful:
- Understanding Downstream Impact: You're modifying a dbt model and need to identify the owners of dependent downstream models and dashboards. The MCP server can provide these details, allowing you to proactively notify them about your upcoming changes.
- Accessing Up-to-Date Table Metadata: You're about to update a table in your data warehouse. Before you proceed, you can query the MCP server to get its latest metadata. This includes information on how the table is currently monitored in Sifflet, whether it's involved in any ongoing incidents, the list of its frequent users, and other relevant operational details.
- Bootstrapping New Asset Monitoring: You're creating a new table (or dbt model) and want to ensure it's well-monitored from the start. You can ask the MCP server to list the Sifflet monitors already created for similar existing assets. The server can then provide the Monitor-as-Code YAML configurations, which you can adapt and deploy.
- Generating a Monitor from a Description: You want to export the YAML configuration of a Monitor you want to create (e.g. "alert when row count drops below 1000 on the 'orders' table"). The
get_monitor_code_by_descriptiontool returns a Monitor-as-Code YAML snippet for a given list of datasets that you can adapt and commit. Note: requiresEditorrole on the targeted domain (see Prerequisites).
Usage
Prerequisites
uv(Python package installer/environment manager)# uv installation script for Linux/MacOS curl -LsSf https://astral.sh/uv/install.sh | sh
- A Sifflet backend running locally or remotely. You will need the following information:
SIFFLET_API_TOKEN: see how to generate one. A token with theViewerrole is enough for most tools. Theget_monitor_code_by_descriptiontool additionally requiresEditorrole on the targeted domain. If you plan to use that tool, generate a token withEditoraccess on that domain.SIFFLET_BACKEND_URL: Full URL to the Sifflet backend for instance:https://<tenant_name>.siffletdata.com/api/
Using with MCP Clients
Cursor
Add the following configuration in the mcp.json. Follow Cursor instructions to set it up.
{
"mcpServers": {
"mcp_server_sifflet": {
"command": "uvx",
"args": ["sifflet-mcp@latest"],
"env": {
"SIFFLET_API_TOKEN": "<access_token>",
"SIFFLET_BACKEND_URL": "https://<tenant_name>.siffletdata.com/api/"
}
}
}
}
Note: You may need to use the full path to the uvx executable in the command field. You can find the full path by running which uvx in your terminal.
Claude Desktop
Follow the instructions in the Claude documentation to set up claude_desktop_config.json.
Then, add the following configuration to your claude_desktop_config.json file:
{
"mcpServers": {
"sifflet-mcp": {
"command": "uvx",
"args": ["sifflet-mcp@latest"],
"env": {
"SIFFLET_API_TOKEN": "<access_token>",
"SIFFLET_BACKEND_URL": "https://<tenant_name>.siffletdata.com/api/"
}
}
}
}
Note: You may need to use the full path to the uvx executable in the command field. You can find the full path by running which uvx in your terminal.
Contributing
For development setup and contribution guidelines, please see CONTRIBUTING.md.
Reporting Problems
If you encounter any problems or have a bug to report, please feel free to open an issue on this GitHub repository. Alternatively, you can reach out to your Sifflet Customer Success team.
Release files for sifflet-mcp 0.1.7
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| sifflet_mcp-0.1.7.tar.gz | 25.6 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| sifflet_mcp-0.1.7-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 33.4 kB
Release files / sifflet_mcp-0.1.7.tar.gz
| Download URL | sifflet_mcp-0.1.7.tar.gz |
|---|---|
| Size | 25.6 kB |
| Tags | Source |
|
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| Download URL | sifflet_mcp-0.1.7-py3-none-any.whl |
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| Tags | Python 3 |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 3, 2026.
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