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Deriva MCP Server

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add_visible_column

Add a column to the visible-columns list for a context in Deriva, handling plain names, foreign keys, or pseudo-columns. Staged until apply_annotations is called.

Instructions

Add a column to the visible-columns list for a specific context.

This is a convenience tool for adding columns without replacing the entire visible-columns annotation. Changes are staged until apply_annotations() is called.

Args: table_name: Name of the table. context: The context to modify. See Contexts below. column: Column to add. Can be: - String: column name (e.g., "Filename") - List: foreign key reference (e.g., ["schema", "fkey_name"]) - Dict: pseudo-column definition (see set_visible_columns) position: Position to insert at (0-indexed). If None, appends to end.

Contexts for visible-columns:

Context

Description

When Used

*

Default for all contexts

Fallback when specific context not set

compact

List/table view

Main record list, search results

compact/brief

Abbreviated list

Inline previews, tooltips

compact/brief/inline

Minimal inline

Foreign key cell display

compact/select

Selection modal

Picker dialogs for foreign keys

detailed

Full record view

Single record page

entry

Data entry forms

Both create and edit forms

entry/create

Create form only

New record creation

entry/edit

Edit form only

Editing existing records

export

Data export

CSV/JSON export

filter

Faceted search

Search sidebar (uses different format)

Returns: JSON with the updated column list for the context.

Examples: # Add column to end of compact view add_visible_column("Image", "compact", "Description")

# Add foreign key reference at position 1
add_visible_column("Image", "detailed", ["domain", "Image_Subject_fkey"], 1)

# Add pseudo-column
add_visible_column("Image", "compact", {
    "source": [{"outbound": ["domain", "Image_Subject_fkey"]}, "Name"],
    "markdown_name": "Subject"
})

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
columnYes
contextYes
positionNo
table_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

No annotations are provided, so the description carries full burden. It discloses that changes are staged until apply_annotations() is called, explains the return value (JSON with updated column list), and describes insertion behavior for position. This is comprehensive behavioral context.

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?

The description is well-organized with intro, args, context table, returns, and examples. Every section adds necessary value, and the formatting enhances readability without being verbose.

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 the tool's complexity (multiple argument types, context variants, staging behavior), the description covers all aspects: purpose, parameter details, context semantics, examples, and return value. It is fully self-sufficient even without annotations.

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?

The schema has 0% description coverage, but the description fully compensates: it explains table_name, context (with a dedicated contexts table), column (three acceptable types with examples), and position (default and behavior when None). This is exemplary parameter documentation.

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 'Add a column to the visible-columns list for a specific context,' providing a specific verb and resource. It distinguishes from the sibling set_visible_columns by positioning itself as a convenience for adding without replacing the entire list.

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

Usage Guidelines4/5

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

The description explicitly notes when to use this tool (adding columns without replacing the whole annotation) and mentions staging via apply_annotations(). It does not explicitly name all alternative tools but the contrast with set_visible_columns is strong and enough for an agent to choose correctly.

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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