graph_query

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

Annotations declare read-only, nondestructive, idempotent. The description adds that it is read-only and discloses rejection of SQL with VALIDATION_ERROR, weakly-grounded anchor filtering, and output format. No contradiction with annotations.

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 concise (4-5 sentences) with front-loaded purpose, followed by constraints, examples, error handling, usage guidance, and output format. Every sentence adds value.

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?

Covers purpose, input constraints, error conditions, output format, and alternative tool. No missing critical elements for this complex tool.

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 coverage is 100% with detailed descriptions. The description adds context like natural language requirement and examples, but the schema already covers parameter meanings sufficiently. Baseline 3 is appropriate.

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 clearly states the tool traces symbol relationships in the dependency graph and returns a subgraph with a Mermaid diagram. It uses a specific verb and resource, and contrasts with the sibling tool 'get_call_graph' for structured call graph exploration.

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?

Explicitly states when to use ('ad-hoc graph exploration') and when not to use ('for structured call graph use get_call_graph instead'). Also provides constraints on input format (natural language only, no SQL) and mentions rejection behavior.

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