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Coalesce-Software-Inc

coalesce-transform-mcp

Official

COA Validate

coa_validate
Read-onlyIdempotent

Scans a Coalesce project's YAML schemas, storage locations, column references, and types to detect and report validation errors.

Instructions

Run coa validate — scans a project's YAML schemas, storage locations, column references, and types.

Known issue: V2 SQL nodes may produce false-positive column reference errors from the Column References scanner. Those don't block coa_dry_run_create / coa_dry_run_run.

Args:

  • projectPath (string, required)

  • workspace (string, optional)

  • include / exclude (string, optional): Node selector

Returns: { command, exitCode, stdout, stderr, json?, coaVersion }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
excludeNoCOA node selector to exclude.
includeNoCOA node selector, e.g., '{ STG_ORDERS }' or '{ location: "SRC" }'. See `coa describe selectors`.
workspaceNoCOA workspace name from workspaces.yml. Defaults to 'dev'.
projectPathYesAbsolute or relative path to the COA project root (the directory containing data.yml).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
jsonNo
stderrYes
stdoutYes
commandYes
exitCodeYes
timedOutYes
coaVersionYes
jsonParseErrorNo
preflightWarningsNo
Behavior4/5

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

Annotations already indicate read-only and idempotent behavior. The description adds value by disclosing a known false-positive issue and detailing the return format (command, exitCode, stdout, etc.), which goes beyond the annotations.

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 concise and front-loaded with the core action. It includes a known issue, arguments, and returns without extraneous details. Every sentence contributes useful information.

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's complexity, the presence of annotations, full schema coverage, and an output schema, the description is fairly complete. It covers what is scanned, a known issue, and the return format, but could benefit from more guidance on interpreting results or prerequisites.

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?

With 100% schema description coverage, the description's argument list largely restates the schema. No additional semantics or examples are provided beyond what is in the schema, so the added value is minimal.

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 clearly states the tool runs `coa validate` and scans YAML schemas, storage locations, column references, and types. It provides a specific verb and resource, but does not explicitly differentiate from sibling tools like `coa_doctor` or `coa_describe`.

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 mentions a known issue with false positives that do not block related tools, offering some context for when validation results may need interpretation. However, it lacks explicit guidance on when to use this tool versus alternative validation or diagnostic 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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