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Start Config

start_config

Launch Stage 3 of the Champollion pipeline by generating dataset YAML configuration files from crop_path and dataset, with optional overrides for output, external config, masks, and external crops.

Instructions

Launch Stage 3: generate Champollion dataset YAML configuration files.

The configs root defaults to //derivatives/champollion_V1/configs, where is the parent of the directory in crop_path; region YAMLs land at {configs root}/dataset//. output overrides the configs root; external_config overrides where the dataset_localization YAML is written (default {configs root}/dataset_localization/). Each is forwarded only when supplied, so the pipeline defaults apply otherwise. When supplied, output and external_config must be absolute paths and, when the MCP client declares roots, inside a declared root. masks is the mask version tag forwarded as --masks; it must match the stage-2 masks (script default canonical_25).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
masksNo
outputNo
datasetYes
crop_pathYes
external_cropsNo
champollion_locNo
external_configNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

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?

With no annotations, the description carries the full burden and does add real behavioral detail: default configs-root derivation, that output/external_config are forwarded only when supplied, and that they must be absolute paths inside a declared root. However, it never says whether existing configs are overwritten, what permissions are required, or what the launch actually produces/schedules.

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?

Purpose is front-loaded in the first sentence, followed by tightly packed defaults and constraint details. Dense but each clause carries information; no filler or restatement of the name.

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?

An output schema exists, so return values need not be described, and the path/default semantics are well covered. But for a launch tool with 0% schema coverage and no annotations, the omission of external_crops and champollion_loc and the lack of any statement about overwrite/ordering behavior leaves gaps.

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 0% across 7 parameters, so the description must compensate. It explains output, external_config, masks, and the crop_path/dataset root derivation well, but external_crops and champollion_loc are left completely undocumented, leaving two parameters with no meaning anywhere.

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 concrete verb and scope: 'Launch Stage 3: generate Champollion dataset YAML configuration files.' The stage number distinguishes it from the sibling start_* pipeline stages (morphologist, cortical_tiles, etc.), though it never names an alternative explicitly.

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?

Usage context is only implied by the 'Stage 3' framing and the note that 'masks ... must match the stage-2 masks,' which is a prerequisite for correct use. There is no statement of when to call this versus start_pipeline or the other start_* tools, and no exclusion guidance.

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