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Coalesce-Software-Inc

coalesce-transform-mcp

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Serialize Workspace Node to Disk YAML

serialize_workspace_node_to_disk_yaml
Read-onlyIdempotent

Convert a cloud workspace node into a disk-ready YAML string for local use, enabling preview of generated SQL without extra round-trips.

Instructions

Fetch a workspace node from the cloud and convert it to the on-disk coa YAML shape (the nodes/*.yml format). Returns both the parsed disk-shape object and a YAML string ready to write to disk.

Use this when an editor or local-dev workflow needs the disk representation of a cloud node — for example, to drop the YAML into a coa project and run coa create --dry-run to preview the generated SQL without an extra round-trip.

The conversion handles the schema diff (top-level → operation: wrapper, nodeID/columnID → stepCounter/columnCounter, sources[] → sourceColumnReferences[], synthesized columnReference per column, fileVersion: 1 header).

UUIDs round-trip; a node serialized this way and pushed back via parse_disk_node_to_workspace_body produces the same cloud body. The converter is lossy on cloud-only metadata (createdAt, updatedAt, etc.) — those are dropped.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nodeIDYesThe node ID to serialize.
workspaceIDYesThe workspace ID containing the node.
Behavior5/5

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

Describes return format (parsed object + YAML string), schema diff handling, round-trip UUIDs, and lossiness on cloud-only metadata, adding value beyond annotations that already declare readOnly and idempotent.

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?

Highly concise and well-structured: first sentence states purpose, second gives usage, third details conversion, fourth mentions round-trip and losses. No superfluous text.

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

Completeness5/5

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

Given no output schema, the description adequately explains the output (parsed disk-shape object and YAML string), transformation details, and limitations (lossy on cloud-only metadata). Sufficient for an agent to understand tool behavior.

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% with clear parameter descriptions. The description does not add additional meaning beyond what the schema provides, maintaining baseline score.

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

Purpose5/5

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

The description clearly states the tool serializes a workspace node to disk YAML format, distinguishing it from siblings like get_workspace_node (fetch only) and parse_disk_node_to_workspace_body (reverse operation).

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

Usage Guidelines5/5

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

Explicitly states when to use: for editor/local-dev workflows needing disk representation, e.g., to run coa create --dry-run. Also implies alternatives by naming reverse tool.

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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