get_persona_content

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

With no annotations, the description carries the full burden and does well. It discloses the return format (markdown), the content composition (expertise, principles, personality, guidelines), and the design philosophy that no analysis is performed—only content is returned. It also details the include_metadata behavior and provides a usage example, offering rich context beyond a mere getter.

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 purpose, design philosophy, args, returns, and an example. It is slightly verbose—the design philosophy and example could be condensed—but every section adds value and the critical information is front-loaded in the first two sentences.

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?

Given the tool's simplicity (2 params, no annotations) and the presence of an output schema, the description is sufficiently complete. It covers parameter meanings, return type, and the intended downstream usage. It does not discuss edge cases like invalid persona names, but that is not essential for a straightforward retrieval 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?

The schema provides zero description coverage for its two parameters, so the description must compensate and does so thoroughly. It explains persona_name with concrete examples ('security-sentinel', 'pragmatic-architect') and specifies that include_metadata controls the presence of a metadata header (name, description, expertise). This gives the agent full semantic understanding of both arguments.

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 verb-resource pair: 'Get full skill content for a specific persona.' It further clarifies that it returns the complete SKILL.md content, and the explicit design philosophy ('returns CONTENT for the LLM to use, not pre-generated analysis') distinguishes it from analysis-oriented sibling tools like suggest_personas_for_query.

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 description states when to use the tool: 'The calling LLM should use this content to analyze queries from that persona's perspective.' It also contrasts with pre-generated analysis, implying when not to rely on it. However, it does not explicitly name alternative sibling tools to choose instead, which would have made the guidance even stronger.

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