Skip to main content
Glama
informatics-isi-edu

Deriva MCP Server

Official
by informatics-isi-edu
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

apply_catalog_annotations

Apply catalog annotations to configure the Chaise web interface for a Deriva catalog, setting up navigation menus, display preferences, and bulk upload features.

Instructions

Apply catalog-level annotations to initialize the Chaise web interface.

Chaise is Deriva's web-based data browser. This method sets up annotations that control how Chaise displays and organizes the catalog, including the navigation bar and display settings.

Navigation Bar Structure: Creates a navigation bar with organized dropdown menus:

  • User Info: Users, Groups, and RID Lease tables

  • Deriva-ML: Core ML tables (Workflow, Execution, Dataset, Dataset_Version, etc.)

  • WWW: Web content tables (Page, File)

  • {Domain Schema}: All domain-specific tables (excludes vocabularies/associations)

  • Vocabulary: All controlled vocabulary tables from ML and domain schemas

  • Assets: All asset tables from ML and domain schemas

  • Catalog Registry: Link to ermrest registry

  • Documentation: Links to ML docs and instructions

Display Settings:

  • Underscores in names displayed as spaces

  • System columns (RID) shown in views

  • Default landing page set to Dataset table

  • Faceted search and record deletion enabled

Bulk Upload: Configures drag-and-drop file upload for asset tables.

When to call: After creating the domain schema and all tables. The menus are dynamically built from the current schema structure.

Args: navbar_brand_text: Text in the navigation bar brand area (default: "ML Data Browser"). head_title: Browser tab title (default: "Catalog ML").

Returns: JSON with status and applied settings.

Example workflow: 1. create_catalog("localhost", "my_project") 2. create_vocabulary("Species", "Types of species") 3. create_asset("Image", ...) 4. apply_catalog_annotations("My ML Project", "ML Catalog")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
head_titleNoCatalog ML
navbar_brand_textNoML Data Browser

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Tool Definition Quality

A4.2/5.0
Behavior3/5

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

There are no annotations, so the description must carry the behavioral burden. It explains what the tool configures (navigation menus, display settings, bulk upload) and mentions dynamic menu building. However, it omits side-effect details such as whether existing annotations are overwritten, whether the operation is idempotent, or any destructive behavior. This partial transparency is adequate but not complete.

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 long but well-organized into labeled sections (Navigation Bar Structure, Display Settings, Bulk Upload, When to call, Args, Returns, Example workflow). It is appropriately detailed for a complex one-time setup tool, and the structure makes it scannable. Slightly verbose but earned.

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?

The description covers purpose, detailed behavior, parameter semantics, when to call, and an example workflow. The output schema exists, so return values are not critical to describe. Missing some side-effect context (e.g., overwriting existing annotations) but otherwise comprehensive for a catalog initialization tool.

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 description coverage is 0%, but the description compensates with an 'Args' section explaining both parameters (navbar_brand_text and head_title) with their meaning and defaults. This provides clear semantic value beyond the bare schema properties.

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 it 'Apply catalog-level annotations to initialize the Chaise web interface' and enumerates specific behaviors (navigation bar, display settings, bulk upload). This distinguishes it from sibling tools like set_table_display or apply_annotations by being catalog-wide and focused on Chaise initialization.

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 includes a 'When to call' section ('After creating the domain schema and all tables') and an example workflow showing the sequence. It does not explicitly mention alternatives or when not to use, but the context is clear enough for an agent to decide.

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