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by Teradata
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Python
Remote

Graph Tracelineage

graph_traceLineage
Read-onlyIdempotent

Trace upstream and downstream dependencies for Teradata objects using wildcard or CSV patterns. Identify impact of changes, data lineage, and object dependencies.

Instructions

Analyse object dependencies in Teradata. Supports wildcards (%) and CSV patterns.

Hybrid implementation — no stored procedure required. Python constructs Teradata recursive CTEs that execute entirely server-side. Only the reachable subgraph crosses the network — not the full edge table.

Examples: 'DB.Table' (single), '%WBC%.%' (wildcard), 'DB.T1,DB.T2' (CSV)

Finds upstream dependencies (what the object depends on) and downstream dependents (what depends on the object). Returns nodes and edges representing the dependency subgraph.

When multiple patterns are provided via CSV, one upstream CTE and one downstream CTE is executed per pattern. Results are merged and deduplicated by Python before assembly.

Use this for:

  • Impact analysis: "What breaks if I change or drop this object?"

  • Lineage tracing: "Where does this data come from?"

  • Dependency discovery: "What does this object use?"

  • Pre-deployment validation: checking impacts before making changes

Arguments: object_name - str: Object name pattern(s). Supports wildcards (%) and CSV format. STRING type — not an array.

                   Single:   'DEV01_StGeo_STD_T.mortgage_account'
                   Wildcard: '%WBC%.%'
                   Multiple: '%WBC%.%,%StGeo%.%'

max_depth_up - int: Maximum levels to traverse upstream (0-10). 0 = no upstream analysis. Default: 3

max_depth_down - int: Maximum levels to traverse downstream (0-10). 0 = no downstream analysis. Default: 3

exclude_objects - str: CSV LIKE patterns to exclude. Matches against DB.Object format. Example: 'PRD_%,%.temp_%' Default: '' (no exclusions)

include_containers - str: CSV of container LIKE patterns to include (whitelist). Empty = all containers. Default: '' (all containers)

edge_repository - str: Edge repository view/table conforming to the Required parameter — no default.

return_format - str: 'detailed' (default), 'summary', or 'edges_only'

Returns: ResponseType: formatted response with dependency analysis results.

detailed response structure: { "nodes": [...], // Unique nodes (deduplicated) "upstream_edges": [...], // One row per upstream edge "downstream_edges":[...], // One row per downstream edge "summary": {...} // Aggregate statistics }

Edge row fields: DependentObjectDBName, DependentObjectName, FQDependentObjectName, ReferencedObjectDBName, ReferencedObjectName, FQReferencedObjectName, Src_Kind, Tgt_Kind, Depth, DependencyPath

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
object_nameYes
max_depth_upNo
return_formatNodetailed
max_depth_downNo
edge_repositoryNo
exclude_objectsNo
include_containersNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.2.1

TDQS

A4.8/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the readOnlyHint and idempotentHint annotations, the description discloses the hybrid implementation, the fact that Python constructs server-side recursive CTEs, that only reachable subgraphs cross the network, and that results are deduplicated and merged. It also explains per-pattern CTE execution, giving a strong behavioral model.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but well-structured with clear sections: overview, implementation notes, examples, use cases, arguments, and return structure. Given the tool's seven parameters and zero schema coverage, every section earns its place and the front-loaded purpose and examples aid quick understanding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description provides a detailed return structure including nodes, upstream/downstream edges, summary fields, and edge row field names. Combined with exhaustive argument documentation, an agent has everything needed to select, invoke, and interpret results without opening schemas.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description fully compensates by documenting all seven parameters with types, defaults, ranges, and examples. It also clarifies that object_name is a STRING, not an array, and explicitly warns that edge_repository is required despite the schema default, which is valuable semantic guidance.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with 'Analyse object dependencies in Teradata', naming a specific verb, resource, and system. It further clarifies that it finds both upstream dependencies and downstream dependents, and provides concrete pattern examples, making it easy to distinguish from sibling graph tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description includes a dedicated 'Use this for' section with concrete scenarios such as impact analysis, lineage tracing, and pre-deployment validation. It does not explicitly name alternative sibling tools or say when not to use this tool, so it stops short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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