generate_advpl_example

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

With no annotations, the description holds the burden for behavioral disclosure. It discloses quality standards ('production-ready', 'modern standards', 'no obsolete patterns') and scope (supported patterns). However, it does not disclose return format, potential failure modes, or any dependencies beyond 'researched material', leaving some ambiguity.

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, front-loaded with the action, and every sentence adds value: action, supported patterns, and quality standards. No redundancy or filler.

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?

The description covers the tool's scope and quality but omits what the output looks like (e.g., a code snippet, multiple examples, file structure). Since there is no output schema, this is a gap. However, given the tool's name and clear purpose, it is adequately scoped for simple generation tasks, making a 3 appropriate.

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 schema already covers all three parameters with clear descriptions and an enum for language, so the description does not need to add much. It adds only minor context like 'SQL Server syntax', which reinforces the SQL parameter but does not meaningfully enhance understanding beyond the schema. Baseline of 3 is appropriate given 100% schema coverage.

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's specific action ('Generate') and resource ('production-ready, modern ADVPL/TL++ code examples'), and enumerates supported patterns (ADVPL, TL++, SQL, REST, SOAP, MVC). This distinguishes it from sibling tools, which are research/search-oriented, by emphasizing code generation.

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 phrase 'based on researched material' provides clear context that this tool should be used after research tools like deep_research or search_protheus_docs. It implicitly sets an expectation for prior information gathering, though it does not explicitly name alternatives or exclusions, which keeps it just below a 5.

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