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Teradata MCP Server

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by Teradata

graph_connectedComponents

Identify all weakly connected components in a dependency graph to find isolated sub-graphs and scope impact analysis. Uses Union-Find partitioning on fetched edges without stored procedures.

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 carries full behavioral burden. It discloses key traits: 'Pure-Python implementation — no stored procedure required', 'Issues a single SQL SELECT', 'performs Union-Find WCC partitioning entirely in the MCP server process.' No contradictions with missing annotations. This is detailed and honest.

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 relatively long but well-structured with clear sections (use cases, arguments, returns). It front-loads the core purpose and implementation detail. Every sentence provides value; while slightly verbose, the length is justified by the technical complexity. Could be trimmed marginally but earns a 4.

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 no output schema, the description covers return values in detail: the response structure with node_details, component_summaries, summary_stats, and field descriptions. It explains algorithm, parameters, and results comprehensively. This is complete for a complex graph 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% (no descriptions in input schema). The description fully compensates by explaining each parameter: container_pattern (with examples and critical type note), exclude_objects (default and matching behavior), edge_repository (pattern for AI-Native Data Products). This adds substantial meaning beyond the bare 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 states the specific purpose: 'Identify all Weakly Connected Components (WCC) in the dependency graph.' It uses a specific verb ('Identify') and resource ('dependency graph'), and clearly defines what a connected component is. It distinguishes from sibling graph tools (e.g., graph_bfsLevels, 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 Guidelines4/5

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

The description explicitly lists six use cases (e.g., 'Understanding graph structure and partitioning', 'Pre-filtering before cycle detection'). While it does not state when NOT to use or name alternatives, the listed use cases provide clear guidance. The context includes many sibling tools, implying different purposes.

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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