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apply_schema_proposal

Apply user-approved schema proposals to register draft fields, merge look-alike names, and organize dictionary improvements; requires explicit consent.

Instructions

Organize unregistered (draft) fields and values, register a new record type (proposal id type_...), or take the repository's dictionary improvements into an organized type (dict_...) — ONLY with the user's explicit consent.

    Never call this on your own initiative. First show the user the proposal from propose_schema (its
    `question`), ask whether to apply it, and call this only after they answer yes. Pass who approved
    (approved_by) and the user's own words (user_answer); both are kept in the schema history.
    descriptions: {field: description} for every field being registered (required).
    labels: {field: {value: [natural-language names]}} for every new enum value (required, e.g.
    {"platform": {"remember": ["리멤버"]}}).
    merges: {draft_field: target_field} to fold look-alike names together (values move with history kept).
    index: fields to index. type_description: a new type's description, if its proposal has none.
    items (dict_... only): the item keys the user picked; default: the proposal's additive items. Items left out
    are not offered again for the same file.
    The data itself was already stored when it was written; this only organizes it.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`index`	No
`items`	No
`labels`	No
`merges`	No
`approved_by`	Yes
`proposal_id`	Yes
`user_answer`	Yes
`descriptions`	No
`type_description`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 7, 2026

TDQS

A4.5/5.0

Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses the hard consent requirement, that approved_by/user_answer are persisted in schema history, that merges keep history while moving values, that omitted items are not re-offered, and that the data was already stored so this only organizes it. It does not cover failure/reversibility behavior on partial application, so not a full 5.

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?

Long but well front-loaded: the consent precondition and the three core operations come first, followed by per-parameter semantics. Sentences are dense but each carries distinct information, with only minor redundancy between the opening list and the later parameter notes.

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 9-parameter, no-annotation mutation tool with an output schema (so return values need not be explained), the description covers purpose, gating, sequence, and per-parameter meaning adequately. A little more on partial-apply or rollback behavior would make it fully self-sufficient.

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

Parameters4/5

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

Schema coverage is 0%, so the description must compensate, and it defines the semantics of nearly every parameter: approved_by (who approved), user_answer (user's own words), descriptions/labels/merges shapes with examples, index, type_description, and items including its default behavior. Only proposal_id is left implicit (its meaning is obvious from the surrounding text).

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?

States specific verbs and resources: organize draft fields/values, register a new record type (type_...), or consolidate dictionary improvements into a dict_... type. It also names the relationship to the sibling propose_schema by casting itself as the applier of that proposal, letting an agent distinguish it without opening a schema.

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 NOT to act ('Never call this on your own initiative') and the required sequence: show the propose_schema question, ask the user, call only after a yes. It names the alternative tool and the consent condition that gates use, leaving nothing to inference.

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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