ffuf_fuzz

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

With no annotations provided, the description takes on full responsibility for behavioral disclosure. It does well by revealing significant traits: auto-selecting common.txt when wordlist is empty, automatically switching to POST when 'data' is set, and using auto-calibrate to filter noise. These go beyond a generic 'fuzzer' label and give the agent concrete expectations about runtime behavior.

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 a compact summary followed by a clear parameter list. Each line addresses one parameter in a consistent 'name: explanation' format. No filler or redundant text—every sentence earns its place. It's well-structured and easy to parse for an AI agent.

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 invocation thoroughly, including all parameters and key behavioral nuances. However, it does not mention what the tool returns (e.g., fuzzing matches, output format, job ID) since there is no output schema. For a complex tool with 10 parameters and no annotations, this is a minor gap but not a critical one because the primary purpose is clearly invocation.

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 description coverage is 0%, so the description must fully compensate. It does, by explaining all 10 parameters with meaningful context beyond their names and defaults: e.g., 'url: target URL with FUZZ keyword', 'wordlist: auto-selects common.txt if empty', 'data: setting this switches to POST automatically'. This is exemplary for a zero-coverage 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 opens with 'Fast web fuzzer using ffuf', which clearly states the tool's purpose with a specific verb ('fuzz') and resource ('web'). It distinguishes itself from sibling tools like gobuster_dir by emphasizing the FUZZ keyword placement, a hallmark of ffuf. The context makes it obvious this is for web content fuzzing, not host discovery or port scanning.

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 explains how to use the tool ('Place FUZZ keyword in URL, headers, or POST data') and covers key options like auto-calibrate and POST switching. However, it does not explicitly contrast with alternatives (e.g., gobuster_dir, http_request) or state when NOT to use it. Usage context is implicit but lacks explicit exclusions or comparisons.

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