Skip to main content
Glama
manzoor-source

Teradata MCP Server

graph_analyseDatabase

Read-onlyIdempotent

Execute four graph analyses—root objects, connected components, cycles, and BFS waves—in one call by sharing a single edge fetch, eliminating multiple round-trips.

Instructions

Composite graph analysis — runs findRootObjects, connectedComponents, detectCycles, and bfsLevels in a single MCP call with ONE shared edge fetch.

This tool eliminates the scalability bottleneck of serial MCP round- trips by combining four graph analyses that would otherwise require four separate tool calls, each independently fetching the same edge set from Teradata.

Performance vs individual tools:

  • 1 SQL round-trip instead of 4 (shared edge fetch)

  • 1 MCP response instead of 4 (eliminates stdio serialisation overhead)

  • Same algorithmic complexity (O(V+E) BFS, O(α·N) Union-Find, O(V+E) DFS)

  • In-memory edge sharing: all analyses operate on the same Python list

Use this for:

  • Full database migration readiness assessment

  • Pre-migration cycle + root + wave analysis in one call

  • Dashboard data population (all four analyses needed simultaneously)

  • Any workflow that would otherwise call 3+ individual graph tools

Arguments: container_pattern - str: CSV LIKE patterns for container scope. Supports wildcards (%) and CSV format. Examples: '%SALES%', '%SALES%,%FINANCE%', 'PROD_%'

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

exclude_objects - str: CSV LIKE patterns to exclude. Default: '' (no exclusions)

top_n_roots - int: Number of top root objects (by downstream dependent count) to include in BFS wave analysis. Default: 4

max_depth_down - int: Maximum downstream BFS hops from roots. Default: 10

max_depth_up - int: Maximum upstream BFS hops from roots. 0 = skip upstream analysis. Default: 0

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). Call graph_edgeContractDDL to generate one. Required parameter — no default.

Returns: ResponseType: single response containing all four analyses:

{ "root_objects": { "objects": [...], "summary": {...} }, "components": { "node_details": [...], "summaries": [...], "stats": [...] }, "cycles": { "details": [...], "summaries": [...], "stats": [...] }, "bfs_waves": { "nodes": [...], "cycle_candidates": [...], "summary": {...} }, "edge_stats": { "total_edges": N, "fetch_time_ms": N } }

Example calls:

Full analysis of Sales and Finance databases

handle_graph_analyseDatabase( conn=connection, container_pattern="%SALES%,%FINANCE%", edge_repository="MY_LINEAGE_DB.EdgeRepository" )

Single database family with top 8 roots

handle_graph_analyseDatabase( conn=connection, container_pattern="%FINANCE%", top_n_roots=8, edge_repository="MY_LINEAGE_DB.EdgeRepository" )

Exclude sandbox schemas

handle_graph_analyseDatabase( conn=connection, container_pattern="PROD_%,STAGE_%", exclude_objects="SANDBOX%,%.temp_%", edge_repository="MY_LINEAGE_DB.EdgeRepository" )

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
top_n_rootsNo
max_depth_upNo
max_depth_downNo
edge_repositoryNo
exclude_objectsNo
container_patternYes
Behavior5/5

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

Annotations declare readOnlyHint=true and idempotentHint=true. The description adds context about performance (1 SQL round-trip instead of 4, in-memory edge sharing) and algorithmic complexity, which is beyond what annotations provide. No contradiction.

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 (purpose, performance, use cases, parameters, return format, examples). It is slightly lengthy but every part adds value. Front-loaded with the main composite purpose.

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 (6 parameters, no output schema, composite of 4 analyses), the description is comprehensive. It explains the return structure with JSON example, details parameters, and provides multiple example calls. No gaps.

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%, so the description carries full burden. It provides detailed explanations for all 6 parameters, including critical notes (e.g., container_pattern must be string not array), default values, and references to other tools (graph_edgeContractDDL). Examples further clarify usage.

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 explicitly states it is a composite graph analysis combining findRootObjects, connectedComponents, detectCycles, and bfsLevels in a single call. It distinguishes itself from sibling tools like graph_findRootObjects by explaining the efficiency gain of eliminating serial round-trips.

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?

It provides a dedicated 'Use this for' section listing specific scenarios (full migration readiness, pre-migration analysis, dashboard population, workflows requiring 3+ individual tools). This clearly guides when to use the composite tool versus the individual sibling tools.

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/manzoor-source/teradata-mcp-server-stc'

If you have feedback or need assistance with the MCP directory API, please join our Discord server