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Teradata

Teradata MCP Server

Official
by Teradata

graph_traceLineage

Trace object dependencies in Teradata to identify upstream sources and downstream impacts. Supports wildcards, CSV patterns, and configurable depth. Find what breaks if a table changes, or where data comes from.

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
max_depth_downNo
exclude_objectsNo
include_containersNo
edge_repositoryNo
return_formatNodetailed
Behavior4/5

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

No annotations provided, so description carries full burden. Discloses hybrid implementation, server-side execution, network optimization, and deduplication behavior. Lacks details on potential side effects or errors.

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

Conciseness4/5

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

Well-structured with clear sections (purpose, examples, usage, parameter details, return structure). Slightly verbose but front-loaded with key purpose.

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?

No output schema, but description details return structure (nodes, edges, summary). Covers all parameters and behavioral nuances, making it complete for a complex tool.

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 coverage is 0%, but description provides thorough explanations for all 7 parameters, including examples, defaults, and constraints (e.g., max_depth_up 0-10). Adds significant meaning beyond schema.

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?

Description states specific verb 'Analyse object dependencies' and resource 'Teradata'. Clearly distinguishes from sibling graph tools like graph_analyseDatabase by detailing hybrid implementation and scope (upstream and downstream).

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?

Explicitly lists use cases (impact analysis, lineage tracing, etc.) but does not mention when not to use or compare to alternatives like graph_bfsLevels.

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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