Loading ToolBench
AI penetration testing agent with MCP server capabilities, supporting tool orchestration, multi-agent workflows, and integration with external MCP servers
PentestAgent defines 6 tools with schemas and descriptions, but critical gaps in naming clarity, parameter documentation, and error handling guidance significantly limit LLM usability. Tool names lack clear action verbs (e.g., 'formulate_strategy' is vague; 'finish' and 'wait_for_agents' are unclear). Descriptions are present but generic and lack recovery guidance. Parameter descriptions are sparse. Schemas exist but lack detail. No evidence of input validation or error categorization. Output schemas are not documented. This server would struggle in production because agents cannot reliably determine when to invoke tools or how to handle failures.
Cancel a running agent. Use if an agent is taking too long or is no longer needed.
Complete the crew task. Automatically waits for all agents, synthesizes their results using LLM, and returns final summary. Call this when task objectives are met.
Define and select a strategic Course of Action (COA) for penetration testing. Evaluates feasibility and selects from multiple options.
Check the current status of a specific agent. Useful for monitoring long-running tasks.
Spawn a new agent to work on a specific task. Use for delegating work like port scanning, service enumeration, or vulnerability testing. Each agent runs independently with access to all pentest tools.
Wait for spawned agents to complete and retrieve their results. Call this after spawning agents to get findings before proceeding.
Tool naming lacks clear action verbs and is often vague. 'finish', 'formulate_strategy', and 'wait_for_agents' do not clearly signal what happens when called. LLMs will struggle to determine when to invoke them.
Parameter descriptions are minimal or absent. Example: 'priority' has no enum values, range constraints (what priority range?), or explanation of how it affects scheduling. 'task' lacks format guidance (free-text, specific syntax, length limits?). 'context' and 'rationale' are underdocumented optional strings.
Schema for 'formulate_strategy' candidates parameter is incomplete. Candidates is an array of objects with 'id', 'name', 'pros', 'cons', 'risk' fields, but the schema does not define these nested fields. LLMs cannot infer the structure and will hallucinate or fail.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | D | 54 | 2026-07-28+ | v2 |
| 2026-03-09 | F | 39 | - | v1 |
No output schemas documented. LLMs cannot plan downstream tool calls without knowing what fields to expect. E.g., does 'get_agent_status' return {status: string} or {status, task, result, error, tools_used}?
No error handling guidance. Tools do not describe what errors are possible, whether they are retryable, or how agents should recover. E.g., what happens if an agent_id does not exist? If cancellation fails? If synthesis times out?
Example values in parameter descriptions (e.g., 'agent-0', 'red, blue, green' if present) are anti-patterns. LLMs reuse example values literally instead of adapting to context. Replace with enum constraints or format declarations.
No idempotency, atomicity, or side-effect documentation. Can 'spawn_agent' be called twice with the same task safely? What happens if 'finish' is called multiple times? What partial failures look like in 'wait_for_agents'?
Optional parameters with complex semantics lack guidance. E.g., 'agent_ids' in 'wait_for_agents' is optional, omitting it waits for 'all agents', but this is not explicitly stated. 'context' in 'finish' is optional, unclear when to provide it or how it changes behavior.