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tigergraph-mcp

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Python
Hybrid

tigergraph__clear_graph_data

DestructiveIdempotent

Clear all vertices and edges from a specific graph while preserving its schema. Use to reset a graph to empty state or remove test data before loading production data. Requires confirmation.

Instructions

Clear all data (vertices and edges) from a specific graph while keeping its schema structure intact. This is a destructive operation that removes all graph data.

Use When: • Resetting a graph to empty state • Clearing test data before loading production data • Starting data reload with same schema

Quick Start:

{
  "graph_name": "MyGraph",
  "confirm": true
}

WARNING: • Deletes ALL vertices and edges in the graph • Operation is PERMANENT and cannot be undone • Must set 'confirm': true to execute • Schema (vertex/edge types) remains intact

Tips: • Preserves schema, only clears data • To delete everything including schema, use 'drop_graph' • Always backup important data first • Can specify 'vertex_type' to clear only specific type

Related Tools: drop_graph, get_vertex_count, delete_nodes

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
confirmNoMust be True to confirm the deletion. This is a destructive operation.
profileNoConnection profile name. Omit to use the active default profile. Use 'list_connections' to see available profiles.
graph_nameNoName of the graph. If not provided, uses default connection.
vertex_typeNoType of vertices to clear. If not provided, clears all data.

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?

Beyond the annotations that already flag destructive behavior, the description adds critical operational warnings: the operation is permanent, cannot be undone, requires 'confirm': true, and preserves schema. This goes above what the structured annotations provide and fully discloses the destructive nature.

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, use cases, quick start, warnings, and tips. It is somewhat long, but every section earns its place for a destructive tool where caution is essential.

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 destructive tool with no output schema, the description covers all the operational context an agent needs: what gets cleared, what remains, confirmation requirement, permanence, and related tools. Nothing essential is missing for correct invocation.

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%, so the input schema already explains each parameter. The description adds a quick-start example and reinforces that 'confirm' must be true, but it does not add substantial new meaning beyond the schema definitions.

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 states a specific verb and resource: clear all data (vertices and edges) from a graph while preserving schema. It also explicitly distinguishes itself from 'drop_graph' by clarifying that the schema remains intact, making the intended operation unmistakable.

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?

The 'Use When' section lists concrete scenarios like resetting a graph or clearing test data. It also gives an explicit alternative: use 'drop_graph' to delete everything including schema, which helps an agent choose correctly among sibling tools.

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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