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tigergraph-mcp

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Python
Hybrid

tigergraph__get_neighbors

Read-onlyIdempotent

Retrieve vertices directly connected to a source vertex via edges for 1-hop graph traversal. Specify vertex type and ID, optionally filter by edge type to discover related entities.

Instructions

Get neighbor vertices connected to a source vertex via edges. Useful for 1-hop graph traversal to find connected entities.

Use When: • Finding vertices directly connected to a vertex • 1-hop traversal (immediate neighbors) • Discovering relationships • Building recommendation lists

Quick Start:

{
  "vertex_type": "Person",
  "vertex_id": "user123",
  "edge_type": "FOLLOWS"
}

Common Workflow:

  1. Have a source vertex ID

  2. Call 'get_neighbors' with vertex info

  3. Optionally filter by edge type

  4. Receive list of connected vertices

Tips: • Simpler than writing a query for 1-hop traversal • Can filter by edge type (e.g., only 'FOLLOWS' edges) • Can specify target vertex type • For multi-hop traversal, use 'run_query' instead

Examples: • Find friends: edge_type='FRIENDS' • Find purchases: edge_type='PURCHASED', target_vertex_type='Product' • Find all connections: omit edge_type

Related Tools: get_node_edges, run_query, add_edge

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of neighbors to return.
profileNoConnection profile name. Omit to use the active default profile. Use 'list_connections' to see available profiles.
edge_typeNoType of edges to traverse (e.g., 'purchased', 'friend_of'). If not provided, traverses all edge types.
vertex_idYesID of the source vertex.
graph_nameNoName of the graph. If not provided, uses default connection.
vertex_typeYesType of the source vertex (e.g., 'Person', 'Product').
target_vertex_typeNoType of target vertices to return. If not provided, returns all types.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.2

TDQS

A4.3/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety. The description adds behavioral context like filtering by edge type and target vertex type, and notes that it's simpler than writing a query. However, it doesn't disclose pagination, limits, error handling, or return format—though these are partially covered by the schema. Given annotation coverage, a 3 is appropriate.

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 sections (Use When, Quick Start, Common Workflow, Tips, Examples, Related Tools). It's longer than a minimal description but each section serves a purpose—examples and workflow guidance are practical. It's front-loaded with the core purpose and uses bullets for scannability, though slightly verbose. A 4 reflects efficient structure without excessive padding.

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 tool has 7 parameters (2 required) and no output schema. The description covers typical usage, provides examples, and points to alternatives for multi-hop traversal. It doesn't explain return format or edge cases, but for a straightforward read-only neighbor lookup, it covers the essentials. Given the complexity and lack of output schema, it's fairly complete, though could mention limit behavior or error scenarios.

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 description coverage is 100%, so baseline is 3. The description adds concrete usage patterns: examples like 'Find friends: edge_type=FRIENDS', 'Find purchases: edge_type=PURCHASED, target_vertex_type=Product', and 'Find all connections: omit edge_type'. It also includes a Quick Start JSON showing required parameters. This goes beyond schema descriptions by clarifying how parameters combine, so a 4 is justified.

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 states a clear verb and resource: 'Get neighbor vertices connected to a source vertex via edges.' It explicitly mentions 1-hop graph traversal and differentiates from siblings by naming alternatives like get_node_edges, run_query, and add_edge. This makes the tool's purpose unmistakable and distinct.

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 'Use When' section lists specific conditions (finding directly connected vertices, 1-hop traversal, discovering relationships). It explicitly says 'For multi-hop traversal, use run_query instead' and mentions get_node_edges as related, giving clear exclusions and alternatives. This is exemplary 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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