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tigergraph

tigergraph-mcp

Official
by tigergraph

tigergraph__has_node

Checks whether a vertex with a specified type and ID exists in the graph, returning a boolean for conditional workflows without fetching full vertex data.

Instructions

Purpose: Check if a vertex (node) exists in the graph without retrieving its full data.

When to Use:

  • Verify a vertex exists before operations

  • Validation in data pipelines

  • Conditional logic based on vertex existence

  • Lightweight existence checks (faster than get_node)

Key Features:

  • Returns simple boolean result (exists: true/false)

  • More efficient than get_node() for existence checks

  • No data transfer overhead

Common Workflows:

  1. Safe operations: has_node() → if true, proceed with get_node()/delete_node()

  2. Validation: Check required vertices exist before adding edges

  3. Conditional creation: If not exists, create with add_node()

Tips:

  • Use this instead of get_node() when you only need existence confirmation

  • Combine with add_node() for upsert logic

  • Faster than catching errors from get_node()

Related Tools:

  • get_node: Retrieve full vertex data if it exists

  • add_node: Create vertex if it doesn't exist

  • delete_node: Remove vertex after confirming existence

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
profileNoConnection profile name. Omit to use the active default profile. Use 'list_connections' to see available profiles.
vertex_idYesThe unique identifier of the vertex
graph_nameNoName of the graph (uses default if not specified)
vertex_typeYesThe type of the vertex to check

Schema Changelog

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

  1. First observedv1.0.2

TDQS

A4.9/5.0
Behavior5/5

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

The description states it returns a boolean, is more efficient than get_node, and transfers no data. It also mentions related tools and safety aspects (upsert logic). No annotations are present, so the description carries the full burden, and it does so thoroughly.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

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

The description is well-organized with clear headings and bullet points. It avoids redundancy and every section adds value—purpose, use cases, features, workflows, tips, and related tools. No fluff or irrelevant information.

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

Completeness5/5

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

For a simple existence-check tool, the description is complete. It covers when to use, behavior, and related operations, and even mentions the return type. The absence of an output schema is compensated by the explicit statement that it returns a boolean.

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 provides detailed descriptions for all four parameters (e.g., profile explains default behavior, vertex_id notes string/integer). The tool description does not add extra parameter semantics beyond the schema, but the schema itself is excellent, so the baseline is raised to 4.

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 purpose: to check if a vertex exists without retrieving full data. It explicitly differentiates from get_node and provides concrete use cases, making the tool's intent unmistakable.

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 'When to Use' section gives explicit scenarios (validation, conditional logic), and 'Common Workflows' and 'Tips' provide actionable guidance on how to combine with other tools. This is comprehensive and directly useful for an agent.

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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