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Teradata MCP Server

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graph_edgeContractDDL

Generate DDL for Graph Edge Contract-compliant tables or views. Produces CREATE TABLE/VIEW scripts with required lineage columns. 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
Behavior4/5

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

Discloses key behaviors: no database connection needed, conn parameter unused, generated DDL includes specific columns, optional columns are ignored by graph tools. Lacks mention of error handling or constraints beyond column requirements.

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 sections, front-loads purpose. However, slightly verbose with detailed examples and repetitive explanations. Still clear and organized.

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 simple scope and presence of output schema, the description fully covers purpose, parameters, behavior, and return values. All essential information is present.

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?

Adds significant meaning beyond the schema: explains target_database with examples, object_name default, output_type options with TABLE vs VIEW details, and return value structure. Schema coverage is 100%, but description enriches understanding.

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 it generates DDL for a Graph Edge Contract-conforming table or view, distinguishing it from sibling graph analysis tools like graph_traceLineage and graph_analyseDatabase.

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?

Provides explicit when-to-use: when an edge repository DDL is needed. Also specifies when NOT to use: within an AI-Native Data Product, where a pre-existing view should be used instead, with an example alternative.

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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