Datalineage Summary

Data Lineage Summary

This skill guides the agent in investigating and summarizing the Data Lineage

graph for a specific focal asset (Table-Level Lineage) or specific fields

(Column-Level Lineage). It provides an intuitive left-to-right walkthrough of

how data enters and leaves the asset, abstracting away complex node and link

details into plain English.

Prerequisites

This skill relies on the **Google Cloud Data Lineage (Knowledge Catalog) MCP

Server** for graph traversal. Ensure you can run search_lineage queries in

both upstream and downstream directions. For detailed connection configurations

and tool schemas, refer to MCP Usage.

Workflow Logic

1. Get Lineage

Fetch the lineage graph in both directions from the focal point (both upstream

and downstream) by making *two separate calls* to the MCP tool: one with

"direction": "UPSTREAM" and another with "direction": "DOWNSTREAM".

• Location Strategy: You MUST use the read_url tool to fetch the

comprehensive list of locations dynamically from the provided

Knowledge Catalog Locations

link. To ensure cross-regional lineage is not missed, always verify the

current list of GCP regions using this link before populating the

locations array. You MUST populate the locations array with all

supported physical regions fetched from this link. You may optionally

additionally determine the asset's specific active region (using bq show

or gcloud storage ls).

• Search Parameters: Use maxDepth = 10, maxResults = 5000 and

maxProcessPerLink = 10 as robust defaults when calling search_lineage.

For example, a DOWNSTREAM call should be formatted like this (expanding the

locations array as needed):

    {
      "parent": "projects/project_id/locations/us",
      "locations": [
        "us",
        "us-central1",
        "us-east1",
        "us-west1",
        "europe-west1",
        "asia-northeast1"
      ],
      "rootCriteria": {
        "entities": {
          "entities": [
            {
              "fullyQualifiedName": "bigquery:project.dataset.table"
            }
          ]
        }
      },
      "direction": "DOWNSTREAM",
      "limits": {
        "maxDepth": 10,
        "maxResults": 5000,
        "maxProcessPerLink": 10
      }
    }

Ensure you make a similar call with "direction": "UPSTREAM" to fetch the

upstream lineage.

• Column-Level Lineage (CLL): The search_lineage tool can find all

Column-Level Lineage (CLL) by configuring the field array. If Table-Level

Lineage (TLL) is requested, configure the call to get CLL links along with

the TLL links by exploiting the "*" wildcard. For example:

    "rootCriteria": {
      "entities": {
        "entities": [
          {
            "fullyQualifiedName": "bigquery:project.dataset.table",
            "field": [
              "*"
            ]
          }
        ]
      }
    }

If evaluating a specific column, replace "*" with the specific column name

(e.g., "efficiency_score").

2. Summarize

Generate the summary using the prompt guidelines below.

• Persona: Act as an expert Data Lineage Analyst generating a concise,

easy-to-understand left-to-right walkthrough of the data flow.

• Structure & Flow: Start immediately with the summary text, structured as

follows:

• Overall Flow Type: State the inferred workflow type and data domain

(e.g., "This appears to be a Feature Engineering workflow...").

• Systems Overview: List the primary systems involved up front. If the

request is for Column-Level Lineage, you MUST explicitly declare that

the scope of the analysis is limited to the specified field up front.

• Upstream Lineage: Use the exact bold header Upstream Lineage:.

Narrative must detail how data arrives at the focal asset, mentioning

key source systems, projects, and processing tasks (e.g., Spark on

Dataproc).

• Downstream Lineage: Use the exact bold header `**Downstream

Lineage:**`. Detail where data goes from the focal asset to final

consumer systems.

• Analysis Metadata: Display the parameters used for the API call to

provide transparency on the boundaries of the summary. The output must

contain:

• Locations Searched: {list_of_locations_queried}
• Parent Location: {parent_path}
• Depth Limit: {maxDepth}
• Process per Link Limit: {maxProcessPerLink}
• Tip for User: A prompt suggesting they can ask to rerun with

expanded locations (if not all were used) or depth.

• Granularity Constraints:
• Prioritize flows between Systems, Projects, and Datasets over individual

files/tables.

• You MUST explicitly list specific asset names (e.g., source tables,

intermediate views, consumer tables) if there are fewer than 5. Do not

just summarize counts if there are fewer than 5; name them explicitly.

Otherwise, if 5 or more, aggregate them by count (e.g., "5 GCS

buckets").

• Only mention counts for *ultimate sources*, *final consumers*, and

*total assets*.

• Do not repeat project names redundantly for every dataset if only one

project is involved.

• Tone: Avoid jargon and generic phrases like "There are distinct factual

points." Be direct and clear. The final output is Markdown.

3. Return the Summary

Return the final summarized output back to the user.

External Documentation

• Google Cloud Knowledge Catalog Data Lineage Documentation
• Use the Data Lineage MCP server
• Knowledge Catalog Data Lineage API Reference