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tigergraph-mcp

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tigergraph__create_graph

Create a new graph in TigerGraph with custom vertex and edge types, defining an independent schema for your data model before loading data.

Instructions

Create a new graph in the TigerGraph database with its schema (vertex types and edge types). Each graph has its own independent schema.

Use When: • Creating a new graph from scratch • Setting up a graph with specific vertex and edge types • Initializing a new project or data model • Defining the structure before loading data

Quick Start:

{
  "graph_name": "SocialNetwork",
  "vertex_types": [
    {
      "name": "Person",
      "primary_id": "id",
      "primary_id_type": "STRING",
      "attributes": [
        {"name": "name", "type": "STRING"},
        {"name": "age", "type": "INT"}
      ]
    }
  ],
  "edge_types": [
    {
      "name": "FOLLOWS",
      "from_vertex": "Person",
      "to_vertex": "Person",
      "directed": true,
      "attributes": [
        {"name": "since", "type": "STRING"}
      ]
    }
  ]
}

Common Workflow:

  1. Use 'list_graphs' to check if graph name is available

  2. Design your vertex types and edge types

  3. Call 'create_graph' with the schema

  4. Use 'show_graph_details' to verify it was created correctly

  5. Start loading data with 'add_node' and 'add_edge'

Vertex Primary Key Options: • Default: auto-generates PRIMARY_ID id STRING with primary_id_as_attribute • Explicit PRIMARY_ID: set primary_id (string) and primary_id_type on vertex type • PRIMARY KEY mode: set primary_key: true on one attribute (not GraphStudio compatible) • Composite key: set primary_id to a list of attribute names, e.g. ["title", "year"] All listed attributes must exist in the attribute list (not GraphStudio compatible) • The key is always queryable as a regular attribute

Tips: • Define all vertex types before edge types • Edge types reference vertex types by name • Set 'directed': false on edge types for undirected edges (default: directed) • Consider using 'get_workflow' for step-by-step guidance

Related Tools: list_graphs, show_graph_details, drop_graph

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
profileNoConnection profile name. Omit to use the active default profile. Use 'list_connections' to see available profiles.
edge_typesNoList of edge type definitions for this graph.
graph_nameYesName of the new graph to create.
vertex_typesYesList of vertex type definitions for this graph.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.2

TDQS

A5/5.0
Behavior5/5

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

The description adds substantial behavioral detail beyond the annotations: graphs have independent schemas, primary_id has multiple modes with GraphStudio compatibility caveats, the key remains queryable as an attribute, and vertex types should be defined before edge types. This materially helps an agent predict side effects and constraints. No contradiction with the provided annotations.

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 long but well-structured with clear headings, a code example, bullet lists, and a workflow. Every section adds actionable information, and the core purpose statement is front-loaded. The length is justified by the complexity of defining a graph schema.

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 tool with no output schema and four parameters, the description covers the essential context: when to use it, how to shape the schema payload, what primary key options exist, compatibility caveats, and how to verify the result with sibling tools. Nothing critical for an agent to invoke it correctly is missing.

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?

Even though the schema already describes all four parameters at a high level, the description deeply enriches the meaning of vertex_types and edge_types with a full JSON quick-start example, primary key options, composite key rules, and the directed edge default. The edge_types parameter, which is only loosely typed as an object array in the schema, is clarified considerably.

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 a specific verb and resource: 'Create a new graph in the TigerGraph database with its schema (vertex types and edge types).' It clearly distinguishes this tool from siblings like list_graphs, drop_graph, and update_schema by framing it as the creation/initialization operation.

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 'Use When' section lists concrete conditions such as 'Creating a new graph from scratch' and 'Defining the structure before loading data.' The 'Common Workflow' explicitly names related tools like list_graphs, show_graph_details, add_node, and add_edge, giving the agent a clear call sequence and 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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