Skip to main content
Glama
Teradata

Teradata MCP Server

Official
by Teradata

graph_detectCycles

Detect circular dependencies in your Teradata dependency graph to validate DAG integrity and identify objects forming cycles.

Instructions

Detect circular dependencies (cycles) in the dependency graph.

Pure-Python implementation — no stored procedure required. Issues a single SQL SELECT to fetch the scoped edge set, then performs WCC partitioning followed by iterative DFS cycle detection entirely in the MCP server process.

Use this tool for:

  • Validating graph integrity (DAG property)

  • Finding objects that form circular references

  • Identifying stub-then-replace code patterns

  • Debugging topological sort hangs

  • Pre-deployment cycle checks

Arguments: container_pattern - str: CSV LIKE patterns for container scope. Supports wildcards (%) and CSV format. Examples: 'DFJ%' — single database family '%WBC%,%StGeo%' — multiple families 'DEV01_%,DEV02_%' — multiple prefixes

exclude_objects - str: CSV LIKE patterns to exclude from the scan. 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 cycle detection results.

Response structure: { "cycle_details": [...], // One row per node per cycle "cycle_summaries": [...], // One row per cycle with path string "summary_stats": [...] // Single aggregate row }

cycle_details row fields: Cycle_Id, Cycle_Pos, Node_FQ, Cycle_Length, Component_Id

cycle_summaries row fields: Cycle_Id, Cycle_Length, Component_Id, Cycle_Path

summary_stats row fields: Cycle_Count, Total_Nodes_In_Cycles, Components_With_Cycles, Edge_Count, Components_Scanned, Summary_Message

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
container_patternYes
exclude_objectsNo
edge_repositoryNo
Behavior3/5

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

No annotations are provided, so the description carries full burden. It discloses implementation details (pure Python, SQL fetch, WCC+DFS) but does not mention performance impact, rate limits, or authentication needs. The description is adequate but not comprehensive.

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, but slightly verbose. Each part serves a purpose, though minor trimming could improve conciseness.

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, no annotations, and 3 parameters, the description is remarkably complete. It covers all parameters, describes return structure in detail, and provides usage context, leaving little ambiguity for an AI agent.

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%, but the description compensates excellently. It provides detailed explanations, examples, and defaults for all three parameters (container_pattern, exclude_objects, edge_repository), adding significant 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 clearly states it detects circular dependencies in a dependency graph, with specific use cases like validating DAG property and identifying circular references. It distinguishes from sibling graph tools by focusing on 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?

The description explicitly lists when to use the tool ('Use this tool for: ...') and provides algorithm context. It does not explicitly state when not to use or mention alternatives, but the guidance is clear and sufficient.

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