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plan_windows

Plan fixed-length training windows over dataset episodes by configuring window size, stride, and episode limits for embodied-AI training.

Instructions

Plan fixed-length training windows over the episodes of a dataset (window, stride, episode/start/end).

Input Schema

NameRequiredDescriptionDefault
pathYes
limitNo
strideNo
windowNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.7/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does not say whether planning writes files or only computes a result, what the return value looks like, or whether any permissions or side effects are involved.

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 a single sentence with no wasted wording, and the core purpose is front-loaded. The parenthetical is somewhat cryptic but does not make the sentence bloated.

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

Completeness2/5

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

With four parameters, no annotations, no output schema, and 0% schema description coverage, the definition is too thin. It omits key details about the input parameters, the expected output plan, and how this differs from related tools like segment_episodes.

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

Parameters2/5

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

Schema description coverage is 0%, so the description should compensate, but it only vaguely names window and stride and mentions episode/start/end, which do not correspond to the actual schema parameters. It leaves path and limit entirely unexplained and gives no units, defaults, or constraints for the parameters it does mention.

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

Purpose4/5

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

The description gives a specific verb ('Plan') and resource ('fixed-length training windows over the episodes of a dataset'), which is enough to understand the core operation. It does not, however, distinguish this from the sibling tool segment_episodes, which likely has overlapping territory.

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

Usage Guidelines2/5

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

There is no explicit guidance on when to use plan_windows versus segment_episodes or any other sibling. The only implied usage is planning windows, but no exclusions or routing conditions are stated.

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