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tigergraph

tigergraph-mcp

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by tigergraph

tigergraph__add_node

Upserts a single vertex into a TigerGraph graph by its primary key, creating it if absent or updating attributes if present. Specify vertex type, ID, and optional attributes to store or modify.

Instructions

Add a single node (vertex) to a TigerGraph graph. This performs an upsert operation - creates a new vertex if it doesn't exist, or updates attributes if it does.

Use When: • Creating a single new entity (user, product, document, etc.) • Updating an existing vertex's attributes • You have individual entities to add (not batch loading)

Quick Start:

{
  "vertex_type": "Person",
  "vertex_id": "user123",
  "attributes": {"name": "Alice", "age": 30}
}

Common Workflow:

  1. Call 'show_graph_details' to understand vertex types and attributes

  2. Use 'add_node' to create individual vertices

  3. Call 'get_node' to verify the vertex was created

  4. Use 'add_edge' to connect this vertex to others

Tips: • For multiple vertices: Use 'add_nodes' instead (more efficient) • Primary key is required (usually the 'id' attribute) • Attribute names must match the schema exactly (case-sensitive) • This is an upsert: existing vertices are updated, not duplicated

More Examples:

// Add a product
{
  "vertex_type": "Product",
  "vertex_id": "prod456",
  "attributes": {"name": "Laptop", "price": 999.99, "category": "Electronics"}
}

// Add a document with minimal attributes
{
  "vertex_type": "Document",
  "vertex_id": "doc789",
  "attributes": {"title": "Report Q4 2024"}
}

Related Tools: • add_nodes - Batch insert multiple vertices • get_node - Retrieve a vertex by ID • delete_node - Remove a vertex • has_node - Check if vertex exists

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
profileNoConnection profile name. Omit to use the active default profile. Use 'list_connections' to see available profiles.
vertex_idYesID of the vertex (primary key value). Format: String or integer depending on schema. Example: 'user123', 'product_456', or 12345 Note: Must be unique within the vertex type.
attributesNoVertex attributes as key-value pairs. Keys must match the vertex type schema. Values should match the expected data types. Example: {'name': 'Alice', 'age': 30, 'email': 'alice@example.com'} Tip: Use 'show_graph_details' to see required attributes and types.
graph_nameNoName of the graph. If not provided, uses the default connection. Tip: Use 'list_graphs' to see available graphs.
vertex_typeYesType of the vertex (must exist in graph schema). Example: 'Person', 'Product', 'Company' Tip: Use 'show_graph_details' to see available vertex types.

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?

Without annotations, the description carries the transparency burden and does well by explaining upsert semantics, that existing vertices are updated rather than duplicated, and that primary keys are required. It does not enumerate error cases or permission requirements, so it falls just short of full transparency.

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 front-loaded with the core purpose and organized with clear headers, bullet lists, and code examples. Each section—Use When, Quick Start, Common Workflow, Tips, More Examples, Related Tools—adds useful context without unnecessary fluff.

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 simple 5-parameter schema with full descriptions and no output schema, the description provides complete context for an agent to call the tool correctly. It includes required parameters, optional parameters, workflow ordering, and relationship to sibling tools.

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 provides 100% description coverage for all five parameters. The description adds value with concrete product/document examples and the case-sensitive attribute-name tip, but most parameter meaning is already present in the schema.

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?

Description opens with 'Add a single node (vertex) to a TigerGraph graph' and explicitly states it is an upsert, distinguishing it from batch alternatives. It clearly scopes to one vertex and names related tools such as add_nodes.

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 single-entity creation and attribute updates, and explicitly says not for batch loading. Tips direct users to add_nodes for multiple vertices, providing clear when-to-use and when-not-to-use 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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