Loading ToolBench
A Python-based tool that generates code scaffolds and technology stack recommendations based on business intent and target platform
This server has a single tool 'generate_scaffold' with a hardcoded implementation. The tool name uses a verb ('generate') which is correct, but lacks critical depth in description and schema. The description (94 chars) is present but generic and does not follow the LLM-optimization guidelines (should be 50-200 chars with WHAT, WHEN, and next steps). The input schema is visible with two parameters (business_intent, platform), both having type and description, which is good. However, the output schema is NOT documented anywhere, the code returns {"code_scaffold": [...], "tech_stack": ...} but there is no formal schema definition visible for agents. The tool is essentially a hardcoded lookup table with string templating; it does not integrate with any actual code generation framework, making it unsuitable for production agent use. No error handling is documented. No validation rules are present (e.g., what happens if platform is invalid). The platform parameter should be an enum, not a free-form string. The business_intent parameter is unbounded and could cause token bloat if excessively long. This appears to be a prototype or POC, not a production-grade tool.
Generates code scaffolds and technology stack recommendations based on business intent and target platform
Output schema is not documented. The tool returns a dict with 'code_scaffold' (list of strings) and 'tech_stack' (string), but there is no formal schema definition for agents to understand the response structure. This forces LLMs to infer the output shape from examples.
Tool description (94 chars) is present but lacks LLM-optimization depth. It does not clearly state WHAT happens, WHEN to call it, or WHAT the agent should do with the output. The description 'Generates code scaffolds and technology stack recommendations based on business intent and target platform' is generic and does not guide tool selection among alternatives.
The 'platform' parameter is a free-form string, not an enum. The code hard-codes valid values ('web', 'mobile', 'api', 'internal', 'automation'), but the schema does not declare these as enum constraints. This invites LLMs to hallucinate invalid platform values (e.g., 'desktop', 'iot'), causing silent fallback to 'Next.js + TypeScript' without feedback.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 43 | 2026-07-28+ | v2 |
No error handling documentation. The tool does not specify what happens if platform is invalid, if business_intent is too long, or if inputs are malformed. Agents receive no guidance on recovery.
The business_intent parameter is unbounded. An LLM could pass a multi-paragraph user request (thousands of tokens), causing the scaffold output to include a truncated intent string ('[:60]' in code). This wastes tokens and risks context window exhaustion.
No composition guarantees. The tool returns a 'code_scaffold' as a list of comments/pseudo-code, not executable code. It's unclear whether the agent should pass this to a code_compiler tool, write it to a file, or use it as a template. Downstream tool dependencies are not documented.