Skip to main content
Glama
informatics-isi-edu

Deriva MCP Server

Official

preview_denormalized_dataset

Preview a wide-table join of related dataset tables. View schema shape, join path, and size estimates, then fetch sample rows from a specific dataset when needed.

Instructions

Preview a denormalized (wide table) view of dataset tables.

Joins related dataset tables into a single wide table. Returns schema shape (columns, join path) and size estimates. Optionally returns actual row data when a dataset and limit are provided.

Modes:

  • No dataset_rid: Returns schema shape + global size estimates. Use this to explore what a denormalized join would look like.

  • With dataset_rid, limit=0: Returns shape + dataset-scoped estimates.

  • With dataset_rid, limit>0: Returns shape + estimates + row preview.

Tables are joined based on their foreign key relationships. Column names are prefixed with the source table name using dots (e.g., "Image.Filename", "Subject.RID"). Intermediate tables needed for the join are auto-discovered.

Args: include_tables: List of table names to include in the join. Tables are joined based on their foreign key relationships. Order doesn't matter - the join order is determined automatically. Add more tables iteratively to expand the denormalized view. dataset_rid: RID of the dataset to preview. If omitted, returns schema shape with global (catalog-wide) row counts. version: Semantic version to query (e.g., "1.0.0"). If not specified, uses the current version. Only used with dataset_rid. limit: Maximum rows to return (default: 0, max: 100). Only used with dataset_rid. Set to 0 for shape and estimates only.

Returns: JSON with columns, join_path, tables (per-table size info), total_rows, total_asset_bytes, total_asset_size. When limit > 0 with a dataset_rid, also includes rows and count.

Example: # Explore schema shape (no dataset needed) preview_denormalized_dataset(["Subject", "Report_HVF"]) -> {"columns": [...], "join_path": ["Report_HVF", "Observation", "Subject"], ...}

# Get dataset-scoped estimates
preview_denormalized_dataset(["Image", "Subject"], dataset_rid="1-ABC")
-> {"columns": [...], "tables": {"Image": {"row_count": 50}}, ...}

# Preview actual rows
preview_denormalized_dataset(["Image", "Subject"], dataset_rid="1-ABC", limit=10)
-> {"columns": [...], "tables": {...}, "rows": [...], "count": 10}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
versionNo
dataset_ridNo
include_tablesYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses key behaviors: automatic join resolution, dot-prefixed column naming, auto-discovery of intermediate tables, and the exact modes of operation. It also clarifies that order doesn't matter for include_tables. While it does not mention permissions, rate limits, or effects on underlying data, the read-only nature of a 'preview' is implied and the behavioral details are substantive.

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 structured with a summary, mode breakdown, Args, Returns, and examples. Every section adds distinct value, and the content is front-loaded with the core purpose. Although lengthy, the length is justified by the tool's three modes and parameter nuances, with no redundant sentences.

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 moderate complexity and the absence of annotations, the description is self-sufficient. It fully explains return fields, the meaning of each mode, parameter interactions, and even provides walkthrough examples. The presence of an output schema is not provided in the prompt, so the description's explicit return-value documentation is essential and well handled.

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 provides only titles and defaults with zero descriptions for parameters. The description's Args section fully compensates, explaining each parameter's purpose, constraints (e.g., limit max 100), and mode-dependent behavior. Concrete examples further illustrate parameter combinations, giving the agent everything needed to construct valid calls.

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 opens with a specific verb and resource: "Preview a denormalized (wide table) view of dataset tables." It clearly distinguishes itself from related tools like preview_table by describing wide-table joins, column prefixes, and schema-shape output, making it easy for an agent to recognize when this tool is appropriate.

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 provides explicit mode-based usage guidance: no dataset_rid for exploring, dataset_rid with limit=0 for scoped estimates, and limit>0 for row previews. It explains when each mode is relevant, but it does not explicitly reference alternative sibling tools or state when not to use this tool in favor of another, so it stops short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/informatics-isi-edu/deriva-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server