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tigergraph

tigergraph-mcp

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by tigergraph

tigergraph__add_nodes

Idempotent

Add multiple vertices of the same type to a TigerGraph graph in one batch, reducing API calls and speeding up data loading for imports and initial population.

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.

  1. First observedv1.0.2

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already mark this as readOnlyHint=false, destructiveHint=false, and idempotentHint=true, so the description only needs to add context. It adds optimal batch sizes (1000-5000), the requirement that all vertices share the same type, and the fact that bulk updates to existing vertices are possible (consistent with idempotent upsert). This is useful behavioral detail beyond the structured fields.

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 organized with clear headings: Use When, Quick Start, Common Workflow, Tips, Warning. It is verbose but each section contributes useful guidance, and the key purpose and efficiency claim are front-loaded. It could be tightened slightly but earns its length.

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?

For a tool with 5 parameters, no output schema, and moderate complexity, this description covers everything an agent needs: purpose, use cases, workflow, parameter tips, and common pitfalls. It also references related tools for verification (show_graph_details, get_vertex_count, add_edges). The only minor gap is not describing the return value, but that is often omitted.

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?

Schema coverage is 100%, so the baseline is 3. The description adds real value by giving a specific SARGraph example (ACCOUNT_ID), batch size recommendations, and common mistakes to avoid, which go beyond the parameter descriptions. This helps the agent choose correct values for vertex_id and structure the vertices array.

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: 'Add multiple nodes (vertices) to a TigerGraph graph in a single batch operation.' It immediately distinguishes from the sibling 'add_node' by noting it is 'significantly more efficient than calling add_node multiple times.' This makes the tool's purpose unmistakable and well-differentiated.

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 dedicated 'Use When' list covering loading multiple vertices, importing data, initial population, and bulk updates. It also names alternatives: single-node 'add_node' for fewer inserts and loading jobs for datasets over 10K vertices. These explicit conditions and exclusions give the agent clear routing 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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