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tigergraph

tigergraph-mcp

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by tigergraph

tigergraph__run_installed_query

Executes a pre-installed GSQL query on a TigerGraph graph using provided parameters, enabling faster repeated runs.

Instructions

Run an installed GSQL query on a TigerGraph graph with parameters. Faster than interpreted queries for repeated execution.

Use When: • Running pre-installed, compiled queries • Queries that are executed frequently • Performance-critical operations • Parameterized queries with different inputs

Quick Start:

{
  "query_name": "getPersonFriends",
  "params": {"personId": "user123", "maxHops": 2}
}

Common Workflow:

  1. Install query once with 'install_query'

  2. Run multiple times with 'run_installed_query' and different params

  3. Much faster than 'run_query' for repeated use

Tips: • Queries must be installed first with 'install_query' • Use 'is_query_installed' to check if query exists • Provide params as dictionary matching query signature • Faster than interpreted queries

Related Tools: install_query, is_query_installed, show_query

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsNoQuery parameters.
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_nameYesName of the installed query.

Schema Changelog

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

  1. First observedv1.0.2

TDQS

A4.1/5.0
Behavior2/5

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

Does not disclose potential side effects of running a query, such as possible data modifications, or any required permissions beyond installation. With no annotations, this missing behavioral context leaves users uninformed about write risk.

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?

Well-organized with sections for use cases, quick start, workflow, tips, and related tools, making it easy to scan. Slight redundancy in the performance note, but overall efficient and not overly verbose.

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?

Covers the core action, use cases, prerequisite (query installed), and provides a concrete example. Does not describe return format or error handling, but that is acceptable given no output schema exists and the tool's behavior is straightforward.

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 already covers all parameters with descriptions, and the Quick Start example clarifies the expected structure of params. The description reinforces the type of params (dictionary) and the optional nature of profile and graph_name, adding usable context.

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?

Clearly states it runs an installed GSQL query on a TigerGraph graph with parameters, specifying the action and resource. Differentiates from the related run_query by emphasizing 'installed' and performance benefits.

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?

Provides explicit 'Use When' conditions covering pre-installed queries, frequent execution, performance-critical scenarios, and parameterized inputs. Contrasts with run_query and advises checking is_query_installed, giving clear decision 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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