Teradata MCP Server

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Python

Remote

Graph Edgecontractddl

graph_edgeContractDDL

Read-onlyIdempotent

Creates DDL for graph edge contract tables or views without a database connection. Choose TABLE for DDL plus sample DML, or VIEW to map an existing lineage source to contract columns.

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

Name	Required	Description	Default
`object_name`	No	Name for the edge table/view. Default: 'EdgeRepository'	EdgeRepository
`output_type`	No	'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
`target_database`	Yes	Database in which to create the edge repository. For AI-Native Data Products this is typically {ProductName}_Semantic. Example: 'StGeoMortgage_Semantic'

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Addedv0.2.1Apr 30, 2026

TDQS

A4.4/5.0

Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses that the conn argument is ignored, output is template text, and TABLE vs VIEW behavior differs (sample DML only for TABLE). It also enumerates the response payload keys, so the agent knows exactly what to expect.

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-organized with sections and a one-line summary up front, and the AI-Native shortcut is valuable context. However, it is somewhat redundant with the schema's parameter descriptions and includes the confusing conn argument.

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?

The description covers purpose, non-execution behavior, column contract, the AI-Native product shortcut, argument defaults, and return payload structure. With an output schema also present and annotations already declaring read-only/idempotent behavior, nothing essential is missing.

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

Parameters2/5

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

Schema coverage is 100%, so the schema already explains target_database, object_name, and output_type. The description mostly repeats that content and, worse, lists a conn argument that is absent from the input schema with additionalProperties:false, which could mislead an agent into passing an invalid parameter.

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 first sentence states a precise action ('Generate DDL') and resource ('Graph Edge Contract-conforming table or view'). It clearly distinguishes this from the sibling graph_* analysis tools by being a DDL-generation utility rather than a graph traversal/analysis tool.

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 explicitly says no database connection is required and no SQL is executed, so the agent knows when this is a safe offline generation step. It also gives a concrete alternative: for AI-Native Data Products, use the existing lineage_graph view directly and pass its fully-qualified name rather than generating DDL.

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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