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A cost and latency optimization router that dispatches prompts to appropriate LLM models based on complexity, budget constraints, and latency requirements.
This server has severe definition quality issues. The single tool 'route_prompt' has a basic description and input schema, but critical gaps in error handling, output documentation, and parameter validation make it unsuitable for production use. The tool performs a hardcoded routing decision based on heuristics (prompt length and keyword matching) with no actual LLM integration, cost estimation accuracy, or latency measurement. Output fields are documented only in example code, not in the schema itself.
Routes a prompt to an appropriate LLM model based on complexity, cost budget, and latency constraints. Returns the dispatched model, estimated cost, and routing reason.
Output schema not documented in tool definition. Only visible in example_usage.py (returns dispatched_model, estimated_cost_usd, routing_reason), but schema itself is missing from the tool registration. LLMs cannot plan downstream tool usage without knowing output structure.
Input schema lacks description for 'prompt_text' parameter. The parameter has a type and basic description ('The input prompt text to be routed'), but no guidance on expected length, format, or content characteristics that trigger routing decisions. Baseline from production tools: 100% of A+ tool params have descriptions, this one is minimal.
No error handling or recovery guidance. The implementation has no error conditions documented, what happens if prompt_text is empty, null, or extremely long? What if budget or latency constraints are impossible to satisfy? LLM gets no actionable error messages to guide retry or fallback behavior.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 39 | 2026-07-28+ | v2 |
Parameter constraints not documented. max_budget_per_1k_tokens defaults to 0.002 (USD), and max_latency_ms defaults to 1500, but no minimum/maximum bounds are specified. An LLM could pass negative values, zero, or absurd values (1 second latency, 1 dollar per token) with no validation guidance in the description.
Tool does not actually route to real models or measure real costs/latency. Implementation is hardcoded: prompt > 2000 chars or contains 'complex reasoning' → FLAGSHIP model; else → LIGHTWEIGHT model. No actual LLM API calls, no real cost data, no real latency measurement. Tool description claims 'estimated cost' and 'routing reason' are real, but they are synthetic placeholders.
Tool description is vague about when to use it vs similar tools. Description says 'Routes a prompt to an appropriate LLM model based on complexity, cost budget, and latency constraints.' But the phrase 'appropriate' is undefined, what makes a model appropriate? Does this tool integrate with a real LLM registry? The description does not clarify the dependency or prerequisite setup.
No documentation of return value structure in the schema itself. Output fields (dispatched_model, estimated_cost_usd, routing_reason) are only visible in example_usage.py. An LLM or client consuming the tool via schema alone has no way to know what fields to expect. Baseline: 100% of A+ tools have documented return types.