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Teradata MCP Server

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by Teradata

graph_edgeContractDDL

Read-onlyIdempotent

Generate DDL for a Graph Edge Contract-conforming table or view, defining required source and target columns. Choose TABLE or VIEW output.

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?

Annotations declare readOnlyHint=true and idempotentHint=true. The description aligns perfectly: 'does NOT require a database connection — it generates DDL text from templates. No SQL is executed.' It also explains the conn parameter is unused, adding transparency beyond annotations. No contradiction.

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?

Well-structured: starts with core purpose, then details required/optional columns, provides a usage shortcut, lists arguments with examples, and describes return value. No fluff; every sentence adds value.

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?

Covers all aspects: behavioral (no DB connection), parameter semantics, return structure, and a practical usage shortcut. For a tool with this complexity and rich annotations, the description is complete and leaves no ambiguity.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3. The description adds value with examples (e.g., 'StGeoMortgage_Semantic'), default values, and differentiation between TABLE and VIEW output types. This extra context justifies a 4.

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 'generates DDL for a Graph Edge Contract-conforming table or view,' specifying a specific verb and resource. It distinguishes from sibling tools like graph_analyseDatabase by emphasizing it is for DDL generation, not analysis.

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?

Explicit when-to-use guidance: 'If you are working within an AI-Native Data Product... you do not need to generate DDL — pass that view... directly.' Provides clear context for when to use this tool versus relying on an existing view, and explains the tool's uniqueness (no database connection needed).

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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