faf_go

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

Despite minimal annotations (only readOnlyHint false, destructiveHint false), the description adds behavioral context: it returns questions, applies answers, updates the score, and implies interactive iteration. It does not detail side effects or error conditions, but adequately describes the interactive protocol.

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?

Two sentences, zero wasted words, front-loaded with the purpose and then details. Every sentence provides critical information.

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 complexity (2 params, nested objects, no output schema), the description fully covers what the tool does, when to use it, how parameters behave, and what it returns (next questions and updated score). No missing critical information.

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%, but the description adds notable value: explains path sets session context for subsequent calls, and answers describes the key-value format and the effect of applying answers (new score returned). This goes beyond the schema descriptions.

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?

Clearly states the tool drives a project.faf to 100% through a guided interview, returning questions and applying answers. This specific verb+resource+process distinguishes it from siblings like faf_auto (auto-detection) and faf_score (just scoring).

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 says to use this tool when auto-detection cannot fill human slots, providing a clear when-to-use and when-not-to-use context. This directly guides the agent to choose this over alternatives like faf_auto.

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