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tigergraph

tigergraph-mcp

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by tigergraph

tigergraph__add_edges

Add multiple edges to a TigerGraph graph in a single batch operation, specifying the edge type and a list of edges with source and target IDs.

Instructions

Add multiple edges (relationships) to a TigerGraph graph in a single batch operation. More efficient than calling 'add_edge' multiple times.

Use When: • Loading multiple relationships • Building graph connections in bulk • Importing relationship data from files • Initial graph construction

Quick Start:

{
  "edge_type": "FOLLOWS",
  "edges": [
    {"from_type": "Person", "from_id": "u1", "to_type": "Person", "to_id": "u2"},
    {"from_type": "Person", "from_id": "u2", "to_type": "Person", "to_id": "u3"}
  ]
}

Common Workflow:

  1. Add all vertices first with 'add_nodes'

  2. Use 'add_edges' to create relationships

  3. Verify with 'get_edge_count'

Tips: • All edges in one call must be same edge type • All referenced vertices must exist • Batch size: 1000-5000 edges per call is optimal • Much faster than individual 'add_edge' calls

Related Tools: add_edge, add_nodes, get_edge_count

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
edgesYesList of edges. Each edge is a dict with source and target info. Must specify 'source_id' (or 'source_vertex_id') and 'target_id' (or 'target_vertex_id'). Can also optionally specify 'source_type' and 'target_type' per edge if different from defaults, though batch edges usually share types. All edges in one batch MUST have the same source/target types due to API limitations. Example: [{'source_id': 'u1', 'target_id': 'p1', 'date': '2023'}]
profileNoConnection profile name. Omit to use the active default profile. Use 'list_connections' to see available profiles.
edge_typeYesType of the edges.
graph_nameNoName of the graph. If not provided, uses default connection.

Schema Changelog

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

  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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It does mention important requirements: 'All referenced vertices must exist' and 'All edges in one call must be same edge type', and it indicates a performance characteristic (batch size 1000–5000). However, it does not state what happens if an edge already exists (e.g., upsert vs. error) or mention any rollback behavior. This slight omission prevents a perfect score.

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-structured with clear section headers (Use When, Quick Start, Common Workflow, Tips, Related Tools) and flows logically from purpose to examples to best practices. It is moderately long but each section adds value. There is minor redundancy: the requirement that all edges must share types appears both in the description and in the schema's 'edges' parameter description, which slightly blurs conciseness.

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?

Given the tool's moderate complexity (4 parameters, no output schema), the description is complete enough for an agent to correctly invoke it. It covers purpose, prerequisites, workflow, and performance guidance. It does not explicitly mention what the tool returns (e.g., a success status or count), but since no output schema is defined and a related verification tool (get_edge_count) is listed, this omission is not critical.

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 describes all four parameters, including a detailed explanation of the 'edges' structure (source_id/target_id, optional type overrides). The description adds a concrete quick-start JSON example and clarifies the constraint that all edges in a batch must share types, reinforcing the schema. It does not add extra meaning for 'profile' or 'graph_name' beyond the schema's own descriptions, but the schema coverage is high, so the baseline is met and slightly enhanced.

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 function: 'Add multiple edges (relationships) to a TigerGraph graph in a single batch operation.' It also explicitly contrasts with the single-edge sibling tool ('More efficient than calling 'add_edge' multiple times'), making its unique purpose obvious.

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' section with concrete scenarios (loading multiple relationships, bulk graph building), a 'Common Workflow' with steps (add vertices first, then edges, then verify), and 'Tips' with batch-size and constraint guidance (all vertices must exist, same edge type per batch). This gives an agent clear decision criteria and execution context.

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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