The server defines 3 tools with explicit schemas visible in src/index.ts. All tools have names, descriptions, and input schemas. However, several critical gaps prevent a higher score: (1) Missing output schemas, no documentation of what each tool returns, forcing LLMs to infer result structure. (2) Parameter descriptions are present but generic in places (e.g., 'Array of tool objects' for the tools param in function_calling lacks detail on required fields). (3) No output field naming conventions enforced, responses from Grok API are not shaped or documented. (4) Error handling descriptions missing, tools do not guide LLM recovery on API failures. (5) Tool names lack clarity on distinguishing vision vs. chat capabilities (image_understanding vs. chat_completion is somewhat clear, but function_calling overlaps conceptually with chat_completion). (6) No idempotency guarantees documented. Tool descriptions avoid stating whether repeated calls are safe. (7) Security: API key injection via XAI_API_KEY is correct (pattern:secret-injection), but no audit logging or permission gating visible.
Missing output schemas for all 3 tools. LLMs cannot plan downstream operations or extract required fields (e.g., tool_call_id from function_calling) without documented response structure.
No error handling guidance. Tool descriptions do not state what errors are possible (API rate limits, invalid model, missing image) or how LLMs should recover. Error responses from Grok API are not shaped or categorized as retryable/fatal.
Parameter 'tools' in function_calling lacks detailed schema. Description says 'Array of tool objects...' but does not specify required fields (type, function.name, function.description, function.parameters). LLMs may construct malformed tool definitions.
Add error-handling section to each tool description. Example for chat_completion: 'On API failure (rate limit, invalid model, auth error), the tool returns a descriptive error. Retry with exponential backoff for rate limits; check model name and API key for auth errors.'
Expand the 'tools' parameter schema in function_calling to explicitly require and describe each field: { type: 'function', function: { name: string (required), description: string (required), parameters: object (JSONSchema) } }. Link to OpenAI function-calling spec.
Document the tool_call_id conditional constraint: 'tool_call_id is required when role=tool, omit for other roles.' Consider splitting the message schema into two: one for user/assistant/system, one for tool.
Remove speculative notes from image_understanding description. Replace 'Note: Grok 3 may support image creation' with: 'Currently supports image analysis only (reading). Image generation is not available in this version.'
Add idempotency statement to all tool descriptions: 'This tool is read-only and idempotent, repeated calls with identical input produce identical results.' (Currently true per risk assessment; make it explicit.)
Undocumented conditional parameter dependency in function_calling. The 'tool_call_id' field is only valid when role='tool', but the schema allows it unconditionally. LLMs may pass it in other contexts.
image_understanding includes speculative capability notes ('Note: Grok 3 may support image creation') in the description. This invites hallucination, remove aspirational features or document them as experimental.
No idempotency guarantees documented. All three tools are stateless read-only (per risk assessment), but descriptions do not state whether repeated calls are safe or if results are cached.
Consider renaming function_calling to invoke_function_with_grok or call_grok_function to follow verb_noun pattern and reduce confusion with chat_completion.
Add per-parameter format constraints in descriptions. Example for model: 'One of: grok-2-latest, grok-3, grok-3-reasoner, grok-3-deepsearch, grok-3-mini-beta. Defaults to grok-3-mini-beta if omitted.' (Already in enum, but explicit text helps LLMs.)
Validate image_url and base64_image mutual exclusivity at runtime and return a clear error: 'Provide either image_url or base64_image, not both or neither.' Document this in the parameter descriptions.
Add timeout expectations to each tool description. Example: 'API calls timeout after 60 seconds. If timeout occurs, retry after 5 seconds.' This prevents LLMs from hanging in a loop.