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limam-B
by limam-B

get_run_status

Retrieve current progress of an ML-Agents training run, including step count, reward trend, and recent checkpoints to monitor performance.

Instructions

Get detailed status of a training run including step progress, reward trend, and checkpoints.

Args: run_id: The run to query. last_n_rewards: Number of recent reward data points to include in the trend. last_n_checkpoints: Number of recent checkpoints to include.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
run_idYes
last_n_rewardsNo
last_n_checkpointsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior3/5

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

Annotations are absent, so the description carries the behavioral burden. It discloses the main result contents (step progress, reward trend, checkpoints) and the effect of the last_n_* parameters. However, it does not explicitly state that the operation is read-only and non-blocking, relying on the verb 'get' to imply this. Given the low-risk nature of a status query, this is adequate but not particularly rich.

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 compact and well-organized: a one-sentence purpose statement followed by a terse Args block. There is no filler, and the tool's purpose is front-loaded so an agent can quickly understand what it does.

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?

For a simple read-style tool with an output schema present, the description adequately covers the input parameters and the returned content areas. It is only incomplete in not addressing sibling selection or explicitly stating its non-blocking nature, but that gap is already captured in usage guidelines. Overall, an agent has enough information to invoke it correctly.

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

Parameters5/5

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

Schema description coverage is 0%, and the description fully compensates by explaining all three parameters: run_id identifies the run, last_n_rewards controls the reward trend length, and last_n_checkpoints controls how many checkpoints are included. This adds real meaning beyond the bare schema properties, titles, and defaults.

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 states a specific verb and resource: 'Get detailed status of a training run' and lists the main content areas (step progress, reward trend, checkpoints). This distinguishes it from the narrower check_step, check_reward, and check_checkpoint siblings, though it does not explicitly differentiate it from get_metrics.

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?

The description gives no guidance on when to use this tool versus wait_for_completion, check_step, check_reward, or get_metrics. The 'get' wording implies an immediate snapshot, but the description never states that it is non-blocking or when it should be preferred over the many related sibling tools.

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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