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Teradata

Teradata MCP Server

Official
by Teradata

graph_connectedComponents

Identify all weakly connected components in a dependency graph to partition it into isolated sub-graphs for structure analysis, impact scoping, and cycle detection pre-filtering.

Instructions

Identify all Weakly Connected Components (WCC) in the dependency graph.

Pure-Python implementation — no stored procedure required. Issues a single SQL SELECT to fetch the scoped edge set, then performs Union-Find WCC partitioning entirely in the MCP server process.

A connected component is a maximal set of nodes where every node can reach every other node when edge direction is ignored. This partitions the graph into isolated sub-graphs.

Use this tool for:

  • Understanding graph structure and partitioning

  • Identifying isolated sub-graphs

  • Scoping downstream impact analysis to a single component

  • Pre-filtering before cycle detection (cycles exist only within a component)

  • Identifying "islands" of related objects for migration or refactoring

  • Estimating blast radius

Arguments: container_pattern - str: CSV LIKE patterns for container scope. Supports wildcards (%) and CSV format. Examples: '%WBC%', '%WBC%,%StGeo%', 'DEV01_%,DEV02_%'

                  CRITICAL: STRING type, not array.
                  CORRECT: container_pattern="%WBC%,%StGeo%"
                  WRONG:   container_pattern=["%WBC%", "%StGeo%"]

exclude_objects - str: CSV LIKE patterns to exclude. Matches against container name (or DB.Object if the pattern contains a dot). Default: '' (no exclusions)

edge_repository - str: Edge repository view/table conforming to the Graph Edge Contract (Src_Container_Name, Src_Object_Name, Src_Kind, Tgt_Container_Name, Tgt_Object_Name, Tgt_Kind columns). For AI-Native Data Products use: '{ProductName}_Semantic.lineage_graph' Call graph_edgeContractDDL to generate a new one. Required — no default.

Returns: ResponseType: formatted response with connected component results.

Response structure: { "node_details": [...], // One row per node with Component_Id "component_summaries": [...], // One row per component "summary_stats": [...] // Single aggregate row }

node_details row fields: Node_FQ, DatabaseName, ObjectName, Component_Id, Object_Kind

component_summaries row fields: Component_Id, Node_Count, Node_List

summary_stats row fields: Component_Count, Node_Count, Edge_Count, Largest_Component, Smallest_Component, Singleton_Count, Summary_Message

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
container_patternYes
exclude_objectsNo
edge_repositoryNo
Behavior5/5

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

With no annotations, the description fully discloses implementation (Pure-Python, single SQL SELECT, Union-Find algorithm) and behavior (nondestructive read), adding valuable transparency.

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 slightly verbose; still every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite no output schema, the description details the response structure and fields, covering all necessary aspects for a complex tool.

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 0%, but the description explains all three parameters with examples, critical type warnings (string vs array), and default values, adding significant meaning beyond the schema.

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 identifies the tool as finding Weakly Connected Components in a dependency graph, defines what a connected component is, and distinguishes from sibling tools like graph_detectCycles by mentioning pre-filtering for cycle detection.

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?

Provides a bullet list of specific use cases (e.g., understanding graph structure, pre-filtering for cycle detection) but does not explicitly state when not to use it or name alternatives.

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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