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updateDataType

Idempotent

Update an existing data type record by ID, for example to rename it. Fields omitted remain unchanged; writes live data.

Instructions

Update a data type - Update an existing datatype record by ID. Fields omitted are untouched. Writes live data.

Use when: renaming a data type.

Required: data_id.

Enums: category_active: 1=active and available for members to use, 0=inactive; limit_available: 0, 1.

See also: createDataType (add new), deleteDataType (remove permanently).

Returns: { status: "success", message: {...updatedRecord} } - the full updated record after changes applied.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
data_idYesData type primary key (required to identify the record)
category_nameNoDisplay name for this content type (e.g. "Single Photo Post", "Multi-Photo Post", "Video Post")
category_activeNo1 = active and available for members to use; 0 = inactive
limit_availableNo1 = membership-plan posting limits apply to this data type; 0 = no per-plan limits
_clear_fieldsNoColumn names to clear to empty string. Available on every `update*` operation. Works on base columns AND EAV/`users_meta` rows (rows preserved with `value=""`). To actually clear a field you MUST use this parameter — sending the field with `""` alone is a no-op (BD drops empty values). To remove a `users_meta` row entirely, use `deleteUserMeta`. See **Rule: Clearing fields**. Example: `_clear_fields: ["h2", "hero_link_url"]`.
Behavior5/5

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

Disclosed 'Writes live data,' 'Fields omitted are untouched,' and detailed the _clear_fields parameter behavior. Annotations confirm non-readonly and non-destructive nature, with no contradictions.

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?

Structured with clear sections (Use when, Required, Enums, See also, Returns) and concise sentences. No redundant information.

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?

Covers all aspects: purpose, parameters, behavior (including clearing fields), return format, and references to related tools. Adequate for a mutation tool without output schema.

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

Parameters5/5

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

Schema coverage is 100%. Description adds value by repeating enums in plain language and explaining the _clear_fields parameter with examples, beyond what schema provides.

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?

Description explicitly states 'Update a data type' with specific verb and resource. It distinguishes from siblings by listing createDataType and deleteDataType as alternatives, making the purpose clear.

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?

Includes 'Use when: renaming a data type.' and 'Required: data_id.' along with 'See also:' references to create/delete alternatives, providing explicit usage context and exclusions.

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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