Skip to main content
Glama
tigergraph

tigergraph-mcp

Official
by tigergraph
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

tigergraph__show_graph_details

Read-onlyIdempotent

Get a graph's schema, queries, loading jobs, and data sources in one place. Filter by detail_type to inspect only a specific category.

Instructions

Show details of a specific graph. By default shows everything (schema, queries, loading jobs, data sources). Use 'detail_type' to show only a specific category.

Use When: • You need a full picture of a graph (schema + queries + jobs) • Starting work with a graph (call this first!) • Checking which queries or loading jobs are installed • Debugging schema or job issues

Quick Start:

{ "graph_name": "SocialNetwork" }

(Shows everything under the graph)

Filter by category:

{ "graph_name": "SocialNetwork", "detail_type": "query" }

Options: 'schema', 'query', 'loading_job', 'data_source'

Tips: • No detail_type → shows all (GSQL LS output) • For structured JSON schema, use 'get_graph_schema' instead • For just graph names, use 'list_graphs' • For vector attributes, use 'list_vector_attributes' instead

Related Tools: get_graph_schema (schema JSON), list_graphs (names only), list_vector_attributes (vector attribute details)

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.
detail_typeNoWhich details to show. Options: 'schema' (vertex/edge types), 'query' (installed queries), 'loading_job' (loading jobs), 'data_source' (data sources). If not provided, shows everything (equivalent to GSQL LS).

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 already declare readOnlyHint=true and destructiveHint=false, so the agent knows this is a safe, read-only operation. The description adds valuable behavioral context: that omitting 'detail_type' shows everything and that it's roughly equivalent to GSQL 'LS' output. However, it doesn't dwell on return format, but the annotation coverage is strong, so this is a minor gap.

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-structured with clear bullet points, JSON examples, and a quick-start section. Very easy to scan and understand. Slightly long (for a description), but each line earns its place by conveying use cases, alternatives, and examples. No fluff.

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?

A read-only, parameterless-required tool with a rich schema (3 parameters, all optional, well-documented) and no output schema. The description provides all necessary info: what it returns, how to filter, alternatives, and JSON examples. Nothing an agent needs to call it correctly is missing.

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 the schema already documents each parameter. The description adds value by providing example JSON payloads and enumerating the 'detail_type' options with their meaning (e.g., 'schema' shows vertex/edge types). It also clarifies the behavior of omitting the parameter, surpassing what the schema alone offers. This goes beyond the baseline.

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 'shows details of a specific graph' with a specific verb and resource. It explicitly differentiates from siblings like 'get_graph_schema' (structured JSON) and 'list_graphs' (names only), and the 'detail_type' parameter's purpose is well explained.

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' bullet points (e.g., 'Starting work with a graph (call this first!)'), lists specific alternative tools and when to use them instead, and explains the behavior of omitting 'detail_type'. This is exemplary guidance that directly helps an agent choose correctly.

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

Deploy Server

Other Tools