compress_ai_readme

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

With no annotations provided, the description carries the full burden and meets it thoroughly. It discloses that transformations are deterministic and pure-text, enumerates exactly what is removed, and explicitly states what is NEVER modified: code blocks, inline code, headings, file paths, URLs, and commands. It also preempts confusion by explaining intentional sentence fragments in the output.

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 organized with clear headers and bullets, front-loading the core purpose and when-to-use conditions before diving into mechanics. Every bullet conveys a distinct, decision-relevant fact, and there is no redundant prose 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 is highly complete for a 3-parameter tool: it explains when to call, what it changes, what it preserves, and how to preview safely. The only minor gap is that it does not specify what the tool returns on a successful non-dry-run write, but that is not essential for the agent to select and invoke it correctly.

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 covers all three parameters at 100%, so the baseline is 3. The description adds meaningful usage context beyond the schema by insisting on dryRun:true first, explaining that projectRoot is where config is read from, and indicating that dryRun returns a compression preview. This enriches the agent's understanding of how parameters should be used.

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 and resource: 'Compress an AI_README.md file using deterministic filler-language removal'. It clearly distinguishes this tool from siblings like validate_ai_readmes, init_ai_readme, and update_ai_readme by naming the exact transformation and explicitly stating it makes no LLM call.

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?

A dedicated 'WHEN TO CALL' section lists concrete triggers: after validation filler-language warnings, high token counts, after init_ai_readme, or whenever reducing token footprint is desired. It also gives a clear operational instruction to use dryRun:true first, which is actionable guidance an agent can follow without inference.

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