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Obrais-cloud

ollama-fleet-mcp

by Obrais-cloud

smart_generate

Generate responses to prompts by routing to a fleet host that already has the model in memory, skipping cold-load latency. Falls back to installed-but-unloaded hosts and errors if no host has the model.

Instructions

Generate a response for model, routed to whichever fleet host already has it loaded in memory (skips cold-load latency). Falls back to any host that has it installed but not loaded, and errors if no host has the model at all.

Args:
    model: model name (e.g. "qwen3:32b").
    prompt: the prompt text to send.
    timeout_sec: max seconds to wait for the generate call (default 120).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelYes
promptYes
timeout_secNo
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It discloses the smart routing behavior, fallback to installed-but-not-loaded hosts, and the error condition when no host has the model. The timeout_sec default is also mentioned. This adds significant behavioral context beyond the schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences plus a compact Args list. Every sentence is necessary: the first explains the core behavior, the second the fallback/error, and the Args list covers parameters. No fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the tool's purpose, routing logic, fallback, errors, and all parameters. The only gap is the lack of any mention of the return value, but given the simple generate response nature and no output schema, this is a minor omission.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has no parameter descriptions, but the Args section covers all three parameters: model with an example ('qwen3:32b'), prompt with purpose, and timeout_sec with default. This fully compensates for the 0% schema coverage, though it could add more detail on constraints or format.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: 'Generate a response for `model`' with a specific routing behavior. It distinguishes itself from the sibling 'generate' by explaining the smart routing to a host with the model already loaded, making the purpose specific and unmistakable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context for when to use this tool: when you want to avoid cold-load latency by routing to a loaded host. It also describes fallback and error conditions. However, it does not explicitly name alternatives like 'generate' or provide exclusions, so it lacks a full when-not-to-use statement.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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