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tigergraph

tigergraph-mcp

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by tigergraph

tigergraph__create_graph

Create a new graph in TigerGraph, defining its vertex and edge types for a custom schema.

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. Dates show when Glama detected each change.

  1. First observedv1.0.2

TDQS

A4.7/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains key behavioral aspects such as vertex primary key modes, GraphStudio compatibility caveats, and edge direction defaults. It does not mention failure behavior if the graph already exists or permission requirements, but the 'new graph' wording and workflow advice to check existing graphs mitigate this.

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, bullet lists, and a concise JSON example. It is front-loaded with the purpose and use cases, followed by workflow and detailed options. Every section serves a practical purpose without unnecessary repetition.

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?

The description provides enough context for an agent to decide when to use this tool, how to structure the input, what to do before/after, and which related tools to consider. It covers common workflows, primary key variations, and compatibility caveats, making it self-sufficient for correct usage.

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?

The input schema already has 100% parameter description coverage. The description adds significant detail for vertex_types and edge_types through a JSON quick-start example and detailed primary key options, which goes beyond the schema descriptions. It does not add much for graph_name or profile, but those are already clear.

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 explicitly states the tool creates a new graph in TigerGraph with its schema, and clarifies that each graph has an independent schema. This clearly distinguishes it from sibling tools like list_graphs, drop_graph, and show_graph_details.

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 description provides a 'Use When' section, a 'Common Workflow' with numbered steps, related tools, and tips. It explicitly guides the agent to check availability with list_graphs, verify with show_graph_details, and use get_workflow for step-by-step 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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