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manzoor-source

Teradata MCP Server

by manzoor-source
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Python
Remote

graph_edgeContractDDL

Read-onlyIdempotent

Generates DDL for a Graph Edge Contract table or view without a database connection. 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
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
target_databaseYesDatabase in which to create the edge repository. For AI-Native Data Products this is typically {ProductName}_Semantic. Example: 'StGeoMortgage_Semantic'

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Tool Definition Quality

A4.9/5.0
Behavior5/5

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

Annotations declare readOnlyHint=true and idempotentHint=true. The description reinforces by stating 'no database connection required', 'no SQL is executed', and that the conn parameter is accepted but not used. No contradictions, and adds detail about output types and returned fields.

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 and bullet points, but somewhat verbose with detailed column lists and full example comments. Every sentence adds value, but could be more concise without losing clarity.

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 fully covers the tool's behavior, parameter details, return structure (with output schema listed), and edge cases like the AI-Native shortcut. It is complete enough for an agent to invoke correctly without 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?

Schema coverage is 100%, and the description adds substantial meaning: it explains the purpose of each parameter with examples (e.g., target_database is typically {ProductName}_Semantic), default values, and how output_type affects DDL generation. This goes far beyond the schema's property descriptions.

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?

Clearly states the tool 'generates DDL for a Graph Edge Contract-conforming table or view', which is a specific verb-resource combination. It distinguishes from siblings by emphasizing no database connection needed and provides a shortcut for AI-Native Data Products, differentiating from other graph analysis tools.

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?

Explicitly says when to use ('generate DDL') and when not ('if working within an AI-Native Data Product, pass that view's name directly'). Also clarifies that the conn parameter is unused for compatibility, and provides a concrete example of alternative usage.

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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