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tigergraph-mcp

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Python
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tigergraph__delete_node

DestructiveIdempotent

Delete a single vertex from the graph by its ID, permanently removing it and any connected edges. Use to clean up obsolete data or remove 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.

  1. First observedv1.0.2

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare destructiveHint=true, but the description adds genuinely new behavioral context: the operation is 'permanent and cannot be undone', and 'Connected edges will also be deleted (CASCADE behavior)'. It also advises verifying existence beforehand. These details go well beyond the structured hints and alert the agent to irreversible side effects.

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 longer than average but well-structured with clear sections (Purpose, When to Use, Important Notes, Common Workflows, Tips, Related Tools) and front-loaded purpose. Each section earns its place for a destructive operation; however, there is minor redundancy — the Tips section partially repeats the Workflows section ('Use has_node() first' and 'use delete_nodes() for better performance' both appear twice). Slight tightening would make it flawless.

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 tool's destructive nature, 4 parameters, and no output schema, the description is remarkably complete: it covers what the tool does, when to use it, irreversible consequences, CASCADE edge behavior, a safe-delete workflow, and the relevant alternatives. No output schema exists, so not detailing return values is acceptable. An agent has everything needed to invoke this tool correctly and safely.

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?

Schema description coverage is 100%, with all four parameters (profile, vertex_id, graph_name, vertex_type) already documented in the input schema. The description adds nothing meaningful about parameter semantics — it notes deletion 'by its ID' but does not elaborate on vertex_id format, vertex_type usage, or how profile/graph_name affect the operation. Per the rubric, baseline 3 applies when the schema does the heavy lifting.

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 opens with 'Delete a single vertex (node) from the graph by its ID' — a specific verb, resource, and scope of deletion. It explicitly distinguishes itself from delete_nodes by emphasizing 'single', and the Related Tools section reinforces the contrast. An agent can immediately tell this tool apart from its plural counterpart without inspecting the sibling definitions.

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?

A dedicated 'When to Use' section lists concrete scenarios: removing a specific vertex, cleaning up obsolete data, deleting test data, and business-logic removal. It also gives explicit alternatives: 'delete_nodes()' for bulk deletion with WHERE clauses, and a safe-delete workflow ('has_node() → delete_node() → verify with get_node()'). The guidance is actionable and leaves nothing to inference.

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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