tigergraph__is_query_installed

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is known. The description adds behavioral context beyond annotations: it states the return type (true/false) and compares it to running a query ('Faster than trying to run and catching errors'). This adds value without contradicting the annotations. A score of 4 is appropriate since it provides useful supplementary behavior information.

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

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

The description is well-structured with a clear opening sentence, 'Use When' bullets, a JSON example, and a 'Tips' section. It is concise, front-loaded with the core purpose, and every sentence serves a distinct purpose. The use of formatting (headings, code block) enhances scannability without redundancy.

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

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

For a simple existence check tool with no output schema, the description covers all essential aspects: what it does, when to use it, how to call it with an example, expected return (true/false), and related tools. No critical information is missing for an agent to correctly invoke this tool in a workflow.

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

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

Schema description coverage is 100%, with each parameter (profile, graph_name, query_name) already documented in the schema. The description adds a concrete JSON quick-start example, which illustrates the expected input format and reduces ambiguity. While it doesn't redefine parameter semantics, the example is a valuable addition beyond the schema's formal descriptions, justifying a score above the baseline of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific, actionable statement: 'Check if a query is installed in TigerGraph without running it.' This clearly names the verb, resource, and scope, and distinguishes it from siblings like run_installed_query and install_query. An agent can immediately understand what this tool does and how it differs from related tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The 'Use When' section explicitly lists scenarios: verifying installation, before running a query, and for conditional logic. It also provides a direct recommendation: 'Use before run_installed_query.' Related tools are listed, giving clear routing to alternatives. This is explicit and actionable guidance on when to select this tool.

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