Loading ToolBench
The server defines 5 tools with proper schema structures, reasonable descriptions, and comprehensive parameter coverage. Naming is action-oriented (list_, get_, create_). Schemas are well-formed JSON Schema with type definitions and constraints (e.g., temperature 0-2, top_p 0-1). However, parameter descriptions are frequently generic or missing semantic depth, output schemas are undocumented, and error handling lacks actionable recovery guidance. The tools themselves are well-scoped (no AND compound operations), but descriptions could be optimized for LLM decision-making by including WHEN to use each tool.
Create a chat completion with the Grok API
Create a text completion with the Grok API
Create embeddings for text with the Grok API
Get details about a specific model
List all models available for use with the Grok API
Output schemas are completely undocumented. No tool describes what fields, types, or structure the response will contain. LLMs cannot plan downstream calls or extract specific data without seeing the schema.
Parameter descriptions lack semantic depth and actionable guidance. Examples: 'Sampling temperature (0-2)' uses jargon without explaining what higher vs. lower values do; 'List of tools the model may call' doesn't explain how tools are invoked or expected format; 'Input text to get embeddings for' doesn't clarify if it accepts a string or array. LLMs need plain-language guidance, not parameter names restated.
Tool descriptions do not explain WHEN to use each tool or how they differ. 'Create a chat completion' vs. 'Create a text completion', when should an LLM choose one over the other? Distinguishing features (multi-turn vs. single prompt, structured responses, etc.) are not mentioned. This forces LLMs to guess.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 11 | 2026-07-28+ | v2 |
| 2026-03-09 | D | 57 | - | v1 |
No error handling guidance in tool descriptions or in the code's error formatters. Handlers exist (GrokResourceNotFoundError, GrokRateLimitError, etc.) but returned error messages do not guide LLM recovery: 'Model not found' should include 'Try calling list_models() to find available models'. Current error formatting (e.g., formatGrokError) converts exceptions to strings but provides no recovery hints.
Complex parameters (messages array, tools array, logit_bias object) lack schema documentation and examples. LLMs need to know: what is the structure of each message? What fields are required (role, content)? What is the format of tool definitions? This requires inline schema or reference links, not omission.
Parameter 'model' (required on all completion/embedding tools) has minimal description ('ID of the model to use'). Should explicitly state: call list_models() first if you don't know the model ID, accepted formats (e.g., 'grok-3' vs 'grok-3-latest'), or that invalid IDs will be caught by get_model.