get_example

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

With no annotations, the description carries the full transparency burden. It adds useful context about the quality of the examples (canonical, CI-validated), but it does not disclose potential error behavior, output format, or access requirements. For a simple read-only retrieval tool, this is a partial disclosure but lacks some operational details.

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 three sentences, each serving a distinct purpose: stating the core function, providing usage guidance, and listing the parameter enums. It is front-loaded with the primary action and contains no redundant or filler text.

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 tool with two parameters and no output schema, the description adequately covers purpose, usage, and parameters. It stops short of explicitly stating the return format or error conditions, but the nature of 'example code' is clear. The absence of annotations and output schema makes it slightly less complete than a tool with richer metadata.

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?

The input schema already provides 100% coverage for both parameters, listing the exact enum values for topic and language. The description repeats these values without adding new syntax or format details. The example query mapping is helpful for usage but does not enhance parameter understanding beyond the schema.

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 uses a specific verb ('Return') with a clear resource ('canonical FLOX example code') and scope ('for a topic, filtered optionally by language'). It also explicitly lists the valid topics and languages, distinguishing this tool from siblings like docs_search or scaffold_strategy by emphasizing the CI-validated, canonical nature of the examples.

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?

It explicitly states when to use the tool ('Use this when the user asks show me how to...') and provides a clear directive to prefer these examples over writing fresh code, with a rationale (CI-validated vs. generated code). This gives the agent strong guidance on when to invoke this tool and when to avoid generating code from memory.

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