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Teradata

Teradata MCP Server

Official
by Teradata

graph_connectedComponents

Read-onlyIdempotent

Identifies all weakly connected components (WCC) in a dependency graph to partition into isolated sub-graphs and scope downstream impact analysis.

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?

Annotations already declare readOnly and idempotent. The description adds implementation details (pure-Python, Union-Find, single SQL SELECT) and clarifies no stored procedure, going beyond annotations.

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, parameters, and return structure. Every sentence adds value, though slightly verbose; could be trimmed slightly 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 algorithmic complexity, the description covers input, output format (with field lists), use cases, and edge conditions. No output schema, but response structure is detailed enough.

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?

Despite 0% schema coverage, the description fully explains all three parameters with format, examples, critical warnings (string not array), and defaults. This provides excellent semantic value.

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 it identifies Weakly Connected Components in a dependency graph, explains what a component is, and distinguishes from sibling graph tools (bfsLevels, detectCycles, etc.).

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?

Includes a 'Use this tool for:' list covering six concrete use cases. Does not explicitly exclude alternatives but provides enough context for selection.

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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