Loading ToolBench
An AI assistant that leverages OpenAI's language models with local and external tools for executing Python code, Kubernetes operations, security scanning, web search, and MCP protocol integration
Critical deficiencies across all dimensions. Tools have only brief string descriptions and no formal JSON Schema input specifications. The InputSchema() methods return plain strings ('Python code in string format to execute') rather than structured JSON Schema objects. No parameter types, constraints, ranges, or validation documented. No output schemas defined. No error handling guidance. No enum constraints, no dependencies documented, and generic descriptions provide minimal LLM guidance for tool selection.
Execute kubectl commands against a Kubernetes cluster
Execute Python code in a REPL environment
Search the web using Google
Scan container images for vulnerabilities using Trivy
NO FORMAL JSON SCHEMA for any tool input. InputSchema() returns plain text strings, not JSON Schema objects. Cannot validate parameters, specify types, ranges, or enums.
Descriptions too generic and underdocumented. 'Execute Python code in a REPL environment' (54 chars) lacks WHEN to use, WHAT it returns, and prerequisites. No distinction between python and other possible code execution tools.
Single string parameter with no type specification. All tools accept a bare 'string' with no constraints, format hints, or validation rules. 'Python code' is ambiguous, Python 2 vs 3? What imports available? Max size? Time limit? kubectl command is equally opaque, which verbs/flags allowed?
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 11 | 2026-07-28+ | v2 |
| 2026-03-09 | F | 16 | - | v1 |
No output schemas. GetToolPrompt() builds the tool list string but there is no documented return type or field structure. Agent cannot plan downstream operations or extract needed IDs.
No error handling guidance. ToolFunc() returns (string, error) but there is no recovery guide, error categorization, or actionable guidance. If python code fails, agent gets a raw error, no hint about what to retry or what to ask the user.
python tool marked IRREVERSIBLE but description does not warn agent. No confirmation step, dry-run option, or pre-execution validation. Agent could execute arbitrary Python code (including system commands) without guards.
kubectl tool marked WRITE and can modify cluster state. No permission gates, audit trail, or scope declarations visible. Description does not clarify which kubectl commands are safe vs dangerous. No validation of commands before execution.
google-search tool registration is conditional (InitTools checks env vars). If GOOGLE_API_KEY or GOOGLE_CSE_ID are missing, tool is silently excluded. No error message, no fallback. Agent discovers absence only at call time.
Tool naming: 'python', 'trivy', 'kubectl', 'search' do not start with action verbs (execute_, scan_, run_, query_). LLM cannot infer intent from names alone. 'python' is ambiguous, is it a tool to interpret Python, or a tool to manage Python environments?
No parameter descriptions. The code stores InputSchema as a plain string via InputSchema() method, but no parameter-level documentation. If a user query said 'search for something', would the agent pass a query, terms, keywords, or question? Undocumented.