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tigergraph

tigergraph-mcp

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by tigergraph

tigergraph__add_nodes

Add multiple vertices of the same type to a TigerGraph graph in a single batch, specifying vertex type and a list of vertex objects with primary keys and attributes.

Instructions

Add multiple nodes (vertices) to a TigerGraph graph in a single batch operation. This is significantly more efficient than calling 'add_node' multiple times.

Use When: • Loading multiple vertices of the same type • Importing data from CSV, JSON, or database • Initial data population • Bulk updates to existing vertices

Quick Start:

{
  "vertex_type": "Person",
  "vertices": [
    {"id": "user1", "name": "Alice", "age": 30},
    {"id": "user2", "name": "Bob", "age": 25}
  ]
}

Common Workflow:

  1. Call 'show_graph_details' to understand the schema

  2. Prepare your data with primary keys and attributes

  3. Use 'add_nodes' to load vertices in batches

  4. Call 'get_vertex_count' to verify loading

  5. Use 'add_edges' to create relationships

Tips: • Set 'vertex_id' to match your schema's primary key name (default: 'id') • For SARGraph: vertex_id='ACCOUNT_ID' for Account vertices • All vertices must be the same type • For very large datasets (>10K vertices), consider using loading jobs • Batch size: 1000-5000 vertices per call is optimal

Warning: Common Mistakes: • Missing primary key in one or more vertices • Using wrong vertex_id name (check schema with show_graph_details) • Mixing different vertex types in one call • Attribute name typos (must match schema exactly) • Wrong data types (e.g., string instead of int)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
profileNoConnection profile name. Omit to use the active default profile. Use 'list_connections' to see available profiles.
verticesYesList of vertices to add. Each vertex must contain the primary key field (specified by 'vertex_id' parameter) and other attributes matching the schema. Example with default vertex_id='id': ```json [ {"id": "user1", "name": "Alice", "age": 30}, {"id": "user2", "name": "Bob", "age": 25} ] ``` Example with vertex_id='ACCOUNT_ID': ```json [ {"ACCOUNT_ID": 1001, "COUNTRY": "US", "ACCOUNT_TYPE": "savings"}, {"ACCOUNT_ID": 1002, "COUNTRY": "UK", "ACCOUNT_TYPE": "checking"} ] ``` Note: All vertices will be processed in a single batch operation for efficiency.
vertex_idNoName of the primary key field in the vertex dictionaries. This tells the tool which field contains the vertex ID. Default: 'id'. Set to match your schema's primary key name. Examples: 'id', 'ACCOUNT_ID', 'TX_ID'id
graph_nameNoName of the graph. If not provided, uses default connection.
vertex_typeYesType of the vertices (all vertices must be the same type). Example: 'Person', 'Product' Tip: Use 'show_graph_details' to see available types.

Schema Changelog

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

  1. First observedv1.0.2

TDQS

A4.1/5.0
Behavior2/5

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

With no annotations provided, the description carries the burden of disclosing side effects. It mentions 'Bulk updates to existing vertices' which hints at upsert behavior, but it does not explicitly state whether existing vertices are overwritten or errors occur, nor does it mention any permission or authentication requirements. This lack of explicit behavioral details limits transparency.

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-organized with distinct sections (Use When, Quick Start, Common Workflow, Tips, Warning) that make it easy to scan. While somewhat lengthy, each section provides actionable information and there is no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description supplies a common workflow and tips that situate the tool within a larger process (schema inspection, verification, edge creation). It does not describe return values or partial failure behavior, but since no output schema is present and the operation is straightforward, this is not a critical gap.

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 covers all five parameters with thorough descriptions and examples, achieving 100% coverage. The tool description adds value with specific guidance such as the SARGraph vertex_id example and batch size recommendations, which go beyond the schema text.

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 clearly states the tool's purpose: to add multiple vertices in a single batch operation, and explicitly contrasts it with 'add_node' for efficiency. The verb 'Add' and resource 'nodes' are unambiguous, and the distinction from the sibling tool is immediate.

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 explicit 'Use When' bullet points including loading multiple vertices of the same type and bulk updates. It also advises against using this tool for very large datasets (>10K vertices), recommending loading jobs instead, which clarifies when to prefer 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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