Skip to main content
Glama
Teradata

Teradata MCP Server

Official
by Teradata

graph_findRootObjects

Identify root objects with no upstream dependencies in Teradata databases for downstream impact analysis. Find source tables and base objects to plan migrations, refactoring, or understand data flow origins.

Instructions

Find root objects (objects with no upstream dependencies) in specified containers.

Root objects are ideal starting points for downstream impact analysis as they represent the foundational data sources that nothing else depends upon.

Use this for:

  • Finding starting points for downstream impact analysis

  • Identifying source tables and base objects in data pipelines

  • Discovering independent objects that can be safely analysed in isolation

  • Understanding data flow origins in a schema or database

  • Planning migration or refactoring by identifying foundation objects

Arguments: container_pattern - str: Database/schema pattern(s) to search. SUPPORTS WILDCARDS (%) and CSV.

                  IMPORTANT: This is a STRING parameter (type: str), not an array.
                  Pass multiple patterns as a single comma-separated string.

                  SINGLE CONTAINER:
                  'DEV01_StGeo_STD_T' - Specific database

                  WILDCARDS (%):
                  '%WBC%' - All databases containing WBC
                  'DEV01_%' - All databases starting with DEV01_
                  '%_STD_T' - All databases ending with _STD_T

                  MULTIPLE CONTAINERS (CSV format):
                  '%WBC%,%StGeo%' - All WBC and StGeo databases
                  'DEV01_StGeo_STD_T,DEV02_WBC_STD_T' - Specific databases
                  'DEV01_%,DEV02_%' - All DEV01 and DEV02 databases

                  WHITESPACE HANDLING:
                  Whitespace is automatically trimmed, so these are equivalent:
                  ✅ '%WBC%,%StGeo%' (no spaces)
                  ✅ '%WBC%, %StGeo%' (spaces after commas - OK)

                  HOW TO PASS IN CODE:
                  Python: container_pattern="%WBC%,%StGeo%"
                  JSON: {"container_pattern": "%WBC%,%StGeo%"}

                  CRITICAL: This is a STRING type parameter.
                  ✅ CORRECT: Pass as string: container_pattern="%WBC%,%StGeo%"
                  ❌ WRONG: Pass as array: container_pattern=["%WBC%", "%StGeo%"]

exclude_objects - str: Comma-separated list of patterns to exclude (SERVER-SIDE filter). Matches against DatabaseName.ObjectName format.

                  Common exclusion patterns:
                  'PRD_%,PROD_%' - Exclude production databases
                  '%.temp_%,%.bak_%' - Exclude temporary and backup objects
                  'DFJ%,C_D02%' - Exclude personal/sandbox schemas

                  Performance: Reduces result set and improves query time
                  Default: '' (empty string = no exclusions)

edge_repository - str: Edge repository table/view conforming to the Required parameter — no default.

object_types - str: Comma-separated list of object types to include (optional filter). Examples: 'T' (tables), 'V' (views), 'P' (procedures), 'M' (macros) Multiple: 'T,V' (tables and views only) Empty = all object types included Default: '' (all types)

return_format - str: Output format: 'detailed' or 'summary' 'detailed' (default): Full object list with metadata 'summary': High-level statistics and counts only Default: 'detailed'

Returns: ResponseType: formatted response with root objects + metadata

Example queries that trigger this tool:

  • "Which objects in WBC and StGeo databases have no dependencies?"

  • "Find root objects in DEV01 databases"

  • "What are the starting points for impact analysis in StGeo?"

  • "Show me base tables with no upstream dependencies"

  • "Which objects should I start analysing for downstream impact?"

Example calls:

Find root objects in WBC and StGeo databases

handle_graph_findRootObjects( conn=connection, container_pattern="%WBC%,%StGeo%" )

Find only root tables (no views/procedures)

handle_graph_findRootObjects( conn=connection, container_pattern="DEV01_%", object_types="T" )

Find root objects excluding production and temporary objects

handle_graph_findRootObjects( conn=connection, container_pattern="%WBC%,%StGeo%", exclude_objects="PRD_%,%.temp_%,%.bak_%" )

Quick summary of root objects

handle_graph_findRootObjects( conn=connection, container_pattern="DEV01_StGeo_STD_T", return_format="summary" )

Technical Implementation:

  • Queries the edge repository to find all objects in specified containers

  • Identifies objects that appear as sources but never as targets

  • These are "root" objects - they have no upstream dependencies

  • Results are filtered by exclude_objects and object_types parameters

  • Returns list of root objects suitable for downstream impact analysis

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
container_patternYes
exclude_objectsNo
edge_repositoryNo
object_typesNo
return_formatNodetailed
Behavior4/5

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

No annotations are provided, so the description carries full responsibility. It explains the internal mechanism (queries edge repository, identifies objects that are sources but not targets) and notes performance implications of exclude_objects. It does not explicitly state that the operation is read-only, but the technical details imply no side effects, which is adequate.

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 clear sections (use cases, arguments, examples, technical implementation) and front-loads the core purpose. However, it is verbose with redundant examples (both 'Example queries' and 'Example calls' show similar usage). Slight trimming would improve conciseness 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?

Given the tool's complexity (5 parameters, no output schema, no annotations), the description is remarkably thorough. It covers the concept of root objects, parameter semantics with edge cases, return format options, and multiple usage examples. The technical implementation section adds transparency. No gaps remain for effective use.

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

Parameters3/5

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

Schema coverage is 0%, so the description must compensate. It provides extensive examples, formatting rules, and notes for each parameter, particularly container_pattern. However, there is a critical contradiction: the description states 'edge_repository ... Required parameter — no default,' but the schema lists a default of '' and does not include it in required. This factual error could mislead the agent about mandatory parameters.

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?

Description starts with a clear verb-resource pair: 'Find root objects (objects with no upstream dependencies) in specified containers.' It elaborates on the concept and provides specific use cases that differentiate it from siblings like graph_traceLineage. The purpose is unambiguous and well-scoped.

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

Usage Guidelines4/5

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

The 'Use this for:' section lists five specific scenarios, and example queries provide trigger patterns. However, it does not explicitly state when not to use this tool or mention alternatives for different analysis tasks (e.g., lineage tracing). The guidance is clear but lacks exclusionary context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Teradata/teradata-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server