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informatics-isi-edu

Deriva MCP Server

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clone_catalog_async

Start a background catalog clone by tracing data from a root RID, and get a task ID to track progress for large or cross-server operations.

Instructions

Create an ML workspace by cloning data reachable from a root RID.

Starts the workspace creation in the background and immediately returns a task_id that you can use to check progress.

The operation uses export annotations (if available) to determine which tables and paths to follow from the root RID, then fills in any uncovered tables (vocabularies, associations). Uses async data copying for performance.

Use this for large catalogs or cross-server clones that may take several minutes to complete. Check progress with get_task_status(task_id).

Args: source_hostname: Source server hostname (e.g., "www.facebase.org"). source_catalog_id: ID of the catalog to clone. root_rid: The starting RID from which to trace reachability (e.g., "3-HXMC"). dest_hostname: Destination hostname. If None, uses source hostname. alias: Optional alias name for the new catalog. add_ml_schema: If True, add the DerivaML schema to the clone. asset_mode: How to handle assets: "none", "refs" (default), or "full". copy_annotations: If True (default), copy all annotations. copy_policy: If True (default), copy ACL policies. exclude_schemas: Schemas to exclude from cloning. exclude_objects: Tables ("schema:table") to exclude. reinitialize_dataset_versions: If True, reinitialize dataset versions. orphan_strategy: How to handle orphans: "fail", "delete", "nullify". prune_hidden_fkeys: Skip FKs with hidden reference data. truncate_oversized: Truncate values exceeding index limits. include_tables: Additional tables to include. include_associations: Include association tables. include_vocabularies: Include vocabulary tables. table_concurrency: Max concurrent table copies during fill phase. Lower values reduce server load. Default: 2.

Returns: JSON with task_id and status. Use get_task_status(task_id) to check progress.

Example: clone_catalog_async("www.facebase.org", "1", root_rid="3-HXMC", dest_hostname="localhost", alias="facebase-clone", orphan_strategy="delete") -> {"task_id": "abc123", "status": "started", ...}

# Check progress
get_task_status("abc123")
-> {"status": "running", "progress": {"percent_complete": 45.0, ...}}

# When done
get_task_status("abc123")
-> {"status": "completed", "result": {...}}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
aliasNo
root_ridYes
asset_modeNorefs
copy_policyNo
add_ml_schemaNo
dest_hostnameNo
include_tablesNo
exclude_objectsNo
exclude_schemasNo
orphan_strategyNofail
source_hostnameYes
copy_annotationsNo
source_catalog_idYes
table_concurrencyNo
prune_hidden_fkeysNo
truncate_oversizedNo
include_associationsNo
include_vocabulariesNo
reinitialize_dataset_versionsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It thoroughly explains the async behavior, immediate task_id return, use of export annotations to trace reachability, filling uncovered tables, and async copying for performance. It omits auth/failure-mode details but is otherwise strong.

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 long but well-structured: a clear summary, an organized Args block, Returns, and an Example with task-status progression. Every sentence earns its place in supporting a complex 19-parameter async tool.

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?

The tool is complex, but the description covers purpose, usage scenario, parameter semantics, return format, and progress-checking with concrete examples. The output schema exists, and the description complements it rather than repeating structured data.

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 provides meaningful explanations for all 19 parameters, including defaults, examples for source_hostname and root_rid, and semantics for options like orphan_strategy, asset_mode, and table_concurrency. This fully compensates for the lack of schema-level 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 it creates an ML workspace by cloning data reachable from a root RID, and explicitly notes it starts in the background and returns a task_id. This specific verb+resource+scope distinguishes it from the sibling clone_catalog and other catalog tools.

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 advises using this tool for large catalogs or cross-server clones that may take several minutes, and instructs checking progress with get_task_status(task_id). It does not name clone_catalog as the alternative for smaller jobs, but the context is clear enough to guide selection.

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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