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aflpp.start_fuzz

Start a non-blocking afl-fuzz job in a specified workspace, using your target command and initial corpus to hunt for crashes.

Instructions

Start an afl-fuzz job in the workspace (non-blocking).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
envNo
seedNo
resumeNo
job_nameYes
workspaceYes
ascii_modeNo
mopt_levelNo
target_cmdYes
timeout_msNo
cmplog_pathNo
corpus_nameYes
mode_presetNo
cmplog_levelNo
fuzz_secondsNo
mem_limit_mbNo
power_scheduleNo
input_file_pathNo
old_queue_cycleNo
sanitizer_pathsNo
dictionary_pathsNo
crash_explorationNo
deterministic_onlyNo
Behavior2/5

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

With no annotations, description must disclose side effects and behavior. It only reveals 'non-blocking', but omits what starting a job entails (e.g., process creation, filesystem changes, persistence, error handling, resource consumption), leaving the agent blind to significant mutating behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

One clear, front-loaded sentence with no filler earns high marks for brevity and structure. However, given the tool's complexity, the extreme brevity borders on under-specification, but it remains appropriately concise for a starting point.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 22-parameter asynchronous job launcher with no output schema and no annotations, the description provides almost no contextual completeness: no prerequisites, no return/status info, no param semantics, no relationship to cluster or stop/status tools. The agent cannot safely invoke this tool based on the description alone.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 22 parameters with 0% description coverage, and the description mentions none of them. Required fields like job_name, target_cmd, and corpus_name are not explained, nor are the many optional env/settings parameters, so the agent must infer meaning from raw schema names only.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description uses specific verb 'Start' and identifies resource 'an afl-fuzz job in the workspace', with 'non-blocking' clarifying execution mode. This clearly distinguishes it from sibling start_fuzz_cluster (cluster) and lifecycle tools like stop_fuzz/status.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this over start_fuzz_cluster or prerequisites like initialized workspace, existing corpus, or instrumented target. Single sentence provides no usage context beyond location.

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

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