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informatics-isi-edu

Deriva MCP Server

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add_feature_value_record

Add multi-field feature values to domain objects in Deriva, enabling batch insertion for complex features like diagnosis with confidence scores.

Instructions

Add feature values with multiple fields to one or more domain objects.

For features with multiple columns (e.g., a diagnosis with confidence score), use this tool to provide values for each field. Accepts a list of entries for batch insertion. Use lookup_feature first to see available fields.

Feature columns are dynamically generated based on the feature definition:

  • term_columns: Accept vocabulary term names (strings)

  • asset_columns: Accept asset RIDs (strings)

  • value_columns: Accept direct values (strings, numbers)

Args: table_name: Table the target records belong to (e.g., "Image"). feature_name: Name of the feature (e.g., "Diagnosis"). entries: List of dicts, each with: - target_rid (str, required): RID of the target record. - Plus any feature column names mapped to their values. Use lookup_feature to see available columns and types. execution_rid: Execution RID for provenance (uses active if not provided).

Returns: JSON with status, feature_name, count, execution_rid, rids.

Example: # First check the feature structure: lookup_feature("Image", "Diagnosis") # -> {"term_columns": {"Diagnosis_Type": {...}}, "value_columns": {"confidence": {...}}}

# Then add values (single or batch):
add_feature_value_record("Image", "Diagnosis", [
    {"target_rid": "1-ABC", "Diagnosis_Type": "Normal", "confidence": 0.95},
    {"target_rid": "1-DEF", "Diagnosis_Type": "Abnormal", "confidence": 0.87},
])

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entriesYes
table_nameYes
feature_nameYes
execution_ridNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses dynamic column generation (term_columns, asset_columns, value_columns), the default execution_rid behavior, and the return JSON shape. It doesn't cover idempotency or overwrite semantics, which are important for a write tool, but the core behavior is well explained.

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 longer than average but well-organized with sections (args, returns, example). Each part contributes meaning, though the example is somewhat verbose and could be trimmed. It avoids fluff and maintains structure.

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 dynamic schema, batch operation, and lack of annotations, the description is nearly complete: it includes prerequisite guidance, column type semantics, batch behavior, provenance, and return format. It omits error handling and edge cases, so it is not a 5, but it is highly complete.

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 0%, so the description must compensate and does so thoroughly. It explains each parameter: table_name and feature_name with examples, entries as a list of dicts requiring target_rid plus dynamic feature columns, and execution_rid with its default. This goes far beyond the bare schema.

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's function: 'Add feature values with multiple fields to one or more domain objects.' It gives a concrete example and highlights batch insertion, making the purpose distinct. However, it does not explicitly name sibling tools like add_feature_value to draw a contrast, so I cannot give a 5.

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?

It gives explicit usage context: 'For features with multiple columns... use this tool' and instructs to call lookup_feature first. It also mentions batch insertion. However, it doesn't explicitly state when not to use this tool or name alternatives, so it stops short of full guidance.

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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