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Teradata MCP Server

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by Teradata

graph_edgeContractDDL

Generate DDL for a graph edge contract table or view, including required lineage columns and optional enrichment fields. No database connection needed.

Instructions

Generate DDL for a Graph Edge Contract-conforming table or view.

This tool does NOT require a database connection — it generates DDL text from templates. No SQL is executed. The conn parameter is accepted for ModuleLoader calling convention compatibility but is not used.

Required columns in the generated schema (6): Src_Container_Name, Src_Object_Name, Src_Kind, Tgt_Container_Name, Tgt_Object_Name, Tgt_Kind

Optional enrichment columns (2): Edge_Relationship — nature of the edge (ETL_INPUT, ETL_OUTPUT, DIRECT…) Transformation_Type — process category (ETL, FEATURE_ENG, AGGREGATION…) These are ignored by graph analysis tools but useful for visualisation.

AI-Native Data Product shortcut: If you are working within an AI-Native Data Product, the view {ProductName}Semantic.lineage_graph (Observability Module v1.5) already conforms to this contract. You do not need to generate DDL — pass that view's fully-qualified name directly as edge_repository on any graph* tool. Example: edge_repository='StGeoMortgage_Semantic.lineage_graph'

Arguments: conn: TeradataConnection (unused — accepted for ModuleLoader compatibility). target_database: Database in which to create the edge repository. For AI-Native Data Products this is typically {ProductName}_Semantic. Example: 'StGeoMortgage_Semantic' object_name: Name for the edge table/view. Default: 'EdgeRepository' output_type: 'TABLE' or 'VIEW'. TABLE: generates CREATE TABLE DDL + separate sample DML. Includes all 6 required + 2 optional columns. VIEW: generates a CREATE VIEW template for mapping an existing lineage source to all 8 contract columns. Default: 'TABLE'

Returns: list[dict]: Response payload containing: - ddl: DDL script (CREATE TABLE/VIEW + COMMENTs) - sample_dml: Sample INSERT statements + validation query (TABLE only; absent for VIEW) - output_type: 'TABLE' or 'VIEW' - contract_version: Contract version string

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
target_databaseYesDatabase in which to create the edge repository. For AI-Native Data Products this is typically {ProductName}_Semantic. Example: 'StGeoMortgage_Semantic'
object_nameNoName for the edge table/view. Default: 'EdgeRepository'EdgeRepository
output_typeNo'TABLE' or 'VIEW'. TABLE: generates CREATE TABLE DDL + separate sample DML. Includes all 6 required + 2 optional columns. VIEW: generates a CREATE VIEW template for mapping an existing lineage source to all 8 contract columns. Default: 'TABLE'TABLE

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

With no annotations provided, the description carries full burden. It discloses that no SQL is executed, the conn parameter is unused, and the tool generates DDL from templates. It details required and optional columns, output differences between TABLE and VIEW, and the return structure.

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 (core purpose, arguments, returns) and front-loaded with the main action. It is somewhat verbose but each sentence adds value. Minor redundancy (e.g., conn unused mentioned twice) prevents a perfect score.

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 (DDL generation with contract details), the description covers all necessary aspects: purpose, usage, parameters, return values. It also provides a practical shortcut for AI-Native Data Products. No gaps are evident.

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?

Input schema has 100% description coverage, but the description adds meaning beyond the schema: context for target_database (e.g., typical database naming for AI-Native Data Products), defaults, and detailed behavior for output_type (TABLE vs VIEW differences).

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 starts with a clear verb and resource: 'Generate DDL for a Graph Edge Contract-conforming table or view.' It explicitly states it does not require a database connection and generates DDL from templates, distinguishing it from sibling tools that execute SQL or analyze data.

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

Usage Guidelines5/5

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

The description provides explicit guidance: when to generate DDL (creating an edge repository) and when not to (if a conforming view already exists). It gives an example of passing an existing view name directly and explains the unused conn parameter for compatibility.

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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