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exp-agent

exp-agent

by lfy20040505666
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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get_run_config

Fetch the training configuration for a named run, including iterations, learning rates, and densification settings, to inspect experiment setup and diagnose training issues.

Instructions

Get the training config of one run (iterations, learning rates, densification settings...).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "run": "<run>"
    },
    "name": "get_run_config"
  }
}

Input Schema

NameRequiredDescriptionDefault
runYesRun name exactly as returned by list_runs, e.g. 'lego_baseline'.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
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. The "Get" verb signals a read-only operation and the parenthetical enumerates the returned fields, but nothing is said about behavior when the run name is unknown, or whether the values reflect initial vs. resolved/final settings.

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?

A single front-loaded sentence with no wasted preamble. The trailing ellipsis in the parenthetical is slightly loose but does not obscure meaning.

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

Completeness3/5

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

For a simple one-parameter read tool with full schema coverage this is close to sufficient, and the field list hints at the return shape despite no output schema. However, it is ambiguous about whether the config is the requested or the actually-applied configuration, which matters for a diagnostic tool.

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 coverage is 100% and the single parameter's schema description already specifies "run name exactly as returned by list_runs, e.g. 'lego_baseline'". The tool description adds no further meaning beyond what the schema states, so baseline 3 applies.

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?

States a specific verb and resource ("Get the training config of one run") and names the contents (iterations, learning rates, densification settings). It is naturally distinct from the siblings (list_runs, get_final_metrics, get_metric_curve, compare_runs, diagnose_run), but it never explicitly contrasts itself with them.

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 phrase "of one run" implies this is the single-run lookup versus compare_runs for multi-run work, but that routing is left to inference. There is no explicit when-to-use, when-not-to-use, or prerequisite (e.g. that the run must already exist).

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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