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tigergraph

tigergraph-mcp

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by tigergraph

tigergraph__delete_node

Delete a single vertex from a TigerGraph graph by its type and ID. This permanently removes the node and any connected edges, enabling data cleanup or removal of obsolete entities.

Instructions

Purpose: Delete a single vertex (node) from the graph by its ID.

When to Use:

  • Remove a specific vertex from the graph

  • Clean up obsolete data

  • Delete test data

  • Remove entities based on business logic

Important Notes:

  • Warning: This operation is permanent and cannot be undone

  • Connected edges will also be deleted (CASCADE behavior)

  • Verify the vertex exists before deletion if needed

Common Workflows:

  1. Safe delete: has_node()delete_node() → verify with get_node()

  2. Bulk delete: Use delete_nodes() with WHERE clause instead

Tips:

  • Use has_node() first to verify existence

  • Consider the impact on connected edges

  • For multiple deletions, use delete_nodes() for better performance

Related Tools:

  • delete_nodes: Delete multiple vertices at once

  • has_node: Check if vertex exists before deletion

  • get_node: Verify deletion completed

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
profileNoConnection profile name. Omit to use the active default profile. Use 'list_connections' to see available profiles.
vertex_idYesThe unique identifier of the vertex to delete
graph_nameNoName of the graph (uses default if not specified)
vertex_typeYesThe type of the vertex to delete

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.0.2

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden of side effects. It clearly warns that deletion is permanent, connected edges are cascade-deleted, and existence can be checked first.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-organized with Purpose, When to Use, Important Notes, Workflows, Tips, and Related Tools. It is detailed without redundant filler.

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?

For a simple delete operation, it covers selection, workflow, side effects, and related tools. No output schema is needed because the important success criteria and follow-up actions are described.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already describes all four parameters with 100% coverage. The description reinforces vertex_id/vertex_type but does not add substantial semantic meaning beyond the 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?

States it deletes a single vertex by ID and type, with clear scope and use cases. It explicitly differentiates from bulk deletion via delete_nodes, so an agent can select correctly.

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?

Provides when-to-use scenarios, a recommended workflow with has_node verification, and an explicit alternative for bulk deletion. It also warns about permanence and cascade effects, giving clear operational guidance.

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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