Route prompts to the optimal LLM backend via predictive latency scheduling, returning generated completions.
Route a prompt through DIO's intelligent NLMS scheduler to the optimal backend and return the completion.
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | Target model name (optional) | |
| prompt | Yes | Prompt text to send | |
| max_tokens | No | Maximum tokens to generate (default: 256) | |
| temperature | No | Sampling temperature (default: 0.7) |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It only states the routing action and return of a completion. It does not reveal potential side effects, error conditions, or what 'optimal backend' entails. This is insufficient for a tool with no safety profile.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, front-loaded with the primary action. It is concise with no redundant content. However, it uses the jargon 'NLMS' without explanation, which slightly reduces clarity but does not harm structure.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
There is no output schema, so the description should clarify the return format. It only says 'returns the completion' without specifying structure, pagination, or error behavior. For a tool with four parameters and no annotations, this is incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers 100% of parameters with descriptions, so the schema already documents prompt, model, max_tokens, and temperature. The description adds no additional semantics beyond the schema; it does not explain parameter interactions or defaults beyond what the schema states. Baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action: 'Route a prompt through DIO's intelligent NLMS scheduler to the optimal backend and return the completion.' It clearly identifies the verb, resource, and outcome. It differentiates from siblings implicitly—dio_get_models, dio_predict_latency, dio_cluster_status—by focusing on routing and completion, though it does not explicitly name them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus its siblings. It does not mention conditions, alternatives, or exclusions. An agent cannot infer when to choose this over dio_get_models or dio_predict_latency from the description alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.