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tigergraph

tigergraph-mcp

Official
by tigergraph

tigergraph__drop_graph

Deletes a TigerGraph graph and all its associated data, schema, and jobs. Use this action carefully as it is permanent and irreversible.

Instructions

Drop (delete) a graph and its schema from the TigerGraph database. This permanently removes the graph, its schema, and all data.

Use When: • Removing a graph that's no longer needed • Cleaning up test graphs • Starting fresh with a new schema

Quick Start:

{
  "graph_name": "TestGraph"
}

Warning: DANGER: • This deletes EVERYTHING: schema, vertices, edges, queries, loading jobs • Operation is PERMANENT and cannot be undone • Double-check the graph_name before executing • Consider using 'clear_graph_data' if you only want to remove data

Tips: • Use 'list_graphs' first to confirm the graph name • For production graphs, always backup first • To keep schema but clear data, use 'clear_graph_data'

Related Tools: create_graph, clear_graph_data, list_graphs

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
profileNoConnection profile name. Omit to use the active default profile. Use 'list_connections' to see available profiles.
graph_nameYesName of the graph to drop.

Schema Changelog

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

  1. First observedv1.0.2

TDQS

A4.9/5.0
Behavior5/5

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

The description prominently warns that the operation is permanent and deletes everything (schema, vertices, edges, queries, loading jobs). It also recommends double-checking the graph name and backing up production graphs, fully disclosing the 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.

Conciseness5/5

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

The description is well-organized with clear sections (Use When, Quick Start, Warning, Tips, Related Tools). It is concise, direct, and free of unnecessary filler, making it easy to scan and understand.

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, the description provides comprehensive context: typical use cases, a warning about permanence, tips for safe execution, and links to related tools. It is complete for an operation with no output schema.

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

Parameters4/5

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

Schema already covers both parameters (profile and graph_name) with clear descriptions, achieving 100% coverage. The description adds a concrete JSON example for graph_name, which slightly enhances clarity, moving above the baseline of 3.

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 the tool drops/deletes a graph and its schema, and distinguishes from related tools like clear_graph_data and create_graph. The description explicitly mentions the destructive nature and the scope (schema, data, queries).

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 explicit 'Use When' scenarios, a quick start example, and directly advises using clear_graph_data when only data removal is needed. This gives clear guidance on when to use this tool versus alternatives.

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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