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manzoor-source

Teradata MCP Server

graph_traceLineage

Read-onlyIdempotent

Trace object dependencies in Teradata to identify upstream sources and downstream impacts. Supports wildcards and CSV patterns for impact analysis, lineage tracing, and pre-deployment validation.

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
Behavior5/5

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

Annotations indicate idempotent and read-only, and the description adds valuable behavioral details: hybrid implementation, no stored procedure, only reachable subgraph crosses network, Python deduplication. No contradictions.

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?

The description is well-structured with sections (description, examples, use cases, arguments, returns). It is somewhat lengthy but front-loaded with critical information. Minor redundancy could be trimmed.

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?

Given the tool's complexity (7 parameters, no output schema, 0% schema coverage), the description is remarkably complete. It even provides the return structure and edge row fields, leaving no ambiguity.

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?

Despite 0% schema description coverage, the tool description provides thorough explanations for all 7 parameters, including types, defaults, examples, and formats. This fully compensates for the schema gap.

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 clearly states the tool analyzes object dependencies in Teradata, supporting wildcards and CSV patterns. It distinguishes from sibling tools by focusing on lineage/impact analysis, with specific use cases listed.

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 explicitly lists use cases (impact analysis, lineage tracing, etc.) and provides examples. While it doesn't explicitly say when not to use, the context is clear and helpful.

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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