Skip to main content
Glama
tigergraph

tigergraph-mcp

Official
by tigergraph

tigergraph__get_neighbors

Retrieve vertices directly connected to a source vertex via edges. Use for 1-hop traversal to find neighbors, filter by edge type or target type.

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. Dates show when Glama detected each change.

  1. First observedv1.0.2

TDQS

A4.6/5.0
Behavior4/5

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

With no annotations, the description carries the full burden of explaining behavior. It states that the tool returns a list of connected vertices and supports filtering by edge type and target type. While it implies a read-only operation through the verb 'get', it does not explicitly state that no data modification occurs, which would be ideal for 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (Use When, Examples, Tips, Related Tools) and every sentence conveys useful information. It is slightly longer than strictly necessary, but the examples and tips add practical value for an agent, so the length is justified.

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 that no output schema is provided, the description adequately communicates the expected result ('Receive list of connected vertices') and the effect of optional filters. It does not detail the exact structure of the returned data (e.g., whether it includes edge attributes), but for a simple neighbor-fetch operation this is sufficient. The mention of related tools for more complex queries adds contextual completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema provides descriptions for all 7 parameters, achieving 100% coverage. Each parameter's purpose is clearly explained, including defaults for optional parameters like 'profile' and 'graph_name'. The descriptions of 'edge_type' and 'target_vertex_type' include examples, making their semantics unambiguous.

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: retrieving neighbor vertices connected to a source vertex via edges. It explicitly identifies this as a 1-hop graph traversal and distinguishes it from related tools like 'run_query' for multi-hop traversals, leaving no ambiguity about what the tool does.

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 specific scenarios (finding directly connected vertices, discovering relationships, building recommendation lists). Concrete examples and a 'Related Tools' section further guide the agent on when to choose this tool versus alternatives, providing clear decision criteria.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/tigergraph/tigergraph-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server