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bdbais

io.github.bdbais/routeai

by bdbais

fleet_setup

Probe Ollama servers to set up fleet configuration: list installed models, recommend missing ones with size/hardware, and hold pulls until user approval.

Instructions

Create or replace the fleet configuration by probing the user's Ollama servers: picks installed models per category and lists recommended models that are missing (with size and hardware). Without nodes it only probes the configured machines. Never pull models without the user's approval.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nodesNo
overwriteNoReplace an existing fleet.toml (the old one is kept as a backup).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.7/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses a key safety constraint ('Never pull models without the user's approval') and explains the probing behavior. However, it does not detail side effects beyond 'create or replace' (e.g., backup behavior, reversibility), and the interplay between 'create or replace' and 'without nodes only probes' is slightly ambiguous. Still, it provides a reasonable behavioral sketch.

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

Conciseness4/5

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

The description is two sentences, front-loaded with the core action and then adding a conditional nuance and a safety rule. It is concise and efficient, with no wasted words.

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?

Given the tool has only two parameters and no output schema, the description covers the main purpose, the conditional behavior, and a safety constraint. It does not explain the return value or potential error conditions, but these are not critical for a setup tool with minimal inputs. The minor ambiguity about 'without nodes' prevents a perfect score.

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

Parameters3/5

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

Schema description coverage is 50%, so the description must compensate. It adds meaning for the `nodes` parameter by explaining the behavior when it is absent ('only probes the configured machines'). It does not elaborate on `overwrite`, though the schema already describes it. Overall, it adds some value but does not fully compensate for the coverage gap.

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 primary action ('Create or replace the fleet configuration') and the resource it affects, while also describing the probing and model-listing behavior. This distinguishes it from siblings like fleet_status (status check) and fleet_pull (model pulling).

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

Usage Guidelines3/5

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

The description offers some usage context, notably the conditional 'Without `nodes` it only probes the configured machines,' which clarifies a specific invocation scenario. However, it does not explicitly name alternative tools or state when this tool should be preferred over others, leaving the agent to infer based on the name and context.

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