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manzoor-source

Teradata MCP Server

graph_connectedComponents

Read-onlyIdempotent

Identifies all weakly connected components in a dependency graph using Union-Find, partitioning into isolated sub-graphs for impact analysis and migration scoping.

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
edge_repositoryNo
exclude_objectsNo
container_patternYes
Behavior5/5

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

Annotations already declare readOnlyHint and idempotentHint. The description adds significant behavioral context: 'Pure-Python implementation — no stored procedure required', 'Issues a single SQL SELECT... then performs Union-Find WCC partitioning entirely in the MCP server process.' It also explains what a connected component is, which exceeds annotation information.

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, but it is slightly verbose for the amount of information. Every sentence adds value, though some duplication could be trimmed.

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 complexity (3 parameters, no output schema), the description provides a full return structure with fields for node_details, component_summaries, and summary_stats. It covers all necessary information for an agent to invoke the tool correctly.

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 description coverage is 0%, so the description fully compensates by providing detailed semantics for each parameter: container_pattern format and examples, exclude_objects default and matching logic, edge_repository requirement and pattern. It also includes critical warnings about types (string vs array).

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 'Identify all Weakly Connected Components (WCC) in the dependency graph.' It uses a specific verb ('identify') and specific resource ('dependency graph'), and distinguishes itself from sibling graph tools like graph_bfsLevels and graph_detectCycles by focusing on WCC partitioning.

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 lists when to use this tool (e.g., 'Understanding graph structure and partitioning', 'Pre-filtering before cycle detection'). It provides clear context and alternatives by implying that other graph tools handle different aspects.

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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