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tigergraph__install_query

Install and compile a GSQL query on a TigerGraph graph to enable faster repeated execution and reusable query logic.

Instructions

Install a GSQL query on a TigerGraph graph, compiling it for faster repeated execution.

Use When: • You have a query you'll run multiple times • You want better query performance • Creating reusable query logic • Building query libraries

Quick Start:

{
  "query_text": "CREATE QUERY getPersonFriends(VERTEX<Person> p) FOR GRAPH MyGraph { ... }"
}

Common Workflow:

  1. Write and test query with 'run_query' first

  2. Once working, install with 'install_query'

  3. Run with 'run_installed_query' (faster)

Tips: • Query text should start with 'CREATE QUERY' • Installation compiles the query for better performance • Can define parameters in query signature • Use 'show_query' to view installed query text

Related Tools: run_installed_query, drop_query, show_query

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
profileNoConnection profile name. Omit to use the active default profile. Use 'list_connections' to see available profiles.
graph_nameNoName of the graph. If not provided, uses default connection.
query_textYesGSQL query text to install.

Schema Changelog

Changes observed during successful MCP inspections.

  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?

Annotations declare readOnlyHint=false, idempotentHint=false, and destructiveHint=false. The description adds that installation compiles the query for speed, requires query_text to start with 'CREATE QUERY', and that installed queries can be viewed via show_query. It doesn't state whether installing overwrites an existing query or what permissions are required, but it discloses the core behavior beyond what annotations provide.

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 organized into clear labeled sections—Use When, Quick Start, Common Workflow, Tips, Related Tools—each with a distinct purpose. It is information-dense but every section earns its place; the workflow and tips directly reduce the risk of misuse, and there is no repetition of schema content.

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 moderately complex installation tool, the description covers purpose, usage, workflow, and related tools. It lacks an explicit account of the return value (though there is no output schema), and it doesn't mention how installing affects an existing query of the same name. Overall, the agent has enough context to invoke the tool correctly in the intended workflow.

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 documents all three parameters (query_text, graph_name, profile), so schema coverage is 100%. The description adds a JSON example and the critical tip that query_text should start with 'CREATE QUERY', which goes beyond the schema's generic description and helps the agent form a valid invocation.

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?

States a specific verb and resource: 'Install a GSQL query on a TigerGraph graph, compiling it for faster repeated execution.' It also distinguishes itself from run_query and run_installed_query in the Common Workflow and Related Tools, so an agent can tell what this tool is for without opening other definitions.

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 four concrete triggers (multiple runs, better performance, reusable logic, query libraries), and the Common Workflow explicitly says to test with run_query before installing and run with run_installed_query afterward. This is clear when-to-use guidance and implies the one-off execution case belongs to run_query.

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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