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PurdueRCAC

Globus MCP Server

by PurdueRCAC

compute_batch_submit

Batch submit multiple Python functions to a Globus Compute endpoint, each with its own source code and arguments, sharing a common environment and configuration.

Instructions

Submit multiple Python functions as a batch to a Compute endpoint.

Each task in the list provides its own function source code and arguments. All tasks share the same endpoint and requirements/worker_init config.

Args: endpoint_id: UUID of the target Compute endpoint. tasks: List of task dicts, each containing: - 'function_source': Python source code (required) - 'function_name': Name of the function (required) - 'args': Positional arguments (optional, default []) - 'kwargs': Keyword arguments (optional, default {}) requirements: Shared pip requirements for all tasks. python_version: Python version for the remote venv. user_endpoint_config: Additional endpoint configuration.

Returns: Dict with 'task_ids' (list) and 'function_ids' (dict mapping function_name to function UUID).

Examples: compute_batch_submit( endpoint_id="abcd-1234-...", tasks=[ {"function_source": "def f1():\n return 1", "function_name": "f1"}, {"function_source": "def f2(x):\n return x*2", "function_name": "f2", "args": [21]}, ], )

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endpoint_idYes
tasksYes
requirementsNo
python_versionNo
user_endpoint_configNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

No annotations provided, so description carries full burden. It explains submission process, shared config, and return IDs, but omits execution model (async vs sync), error handling, auth needs, or side effects (e.g., resource creation).

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?

Well-structured with Args, Returns, Examples sections and front-loaded purpose. Slightly verbose in describing shared config, but overall efficient.

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?

Comprehensive for a batch submit tool: covers inputs, output (task_ids and function_ids), and shared config. Missing error scenarios, limits, and permission requirements, but sufficient given output schema existence.

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?

With 0% schema coverage, description fully compensates by detailing every parameter, including nested task fields (function_source, function_name, args, kwargs) and optional configs, plus a concrete example.

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?

Clear verb 'submit' with specific resource 'multiple Python functions as a batch', directly distinguishing from sibling compute_submit (single function). The term 'batch' and mention of shared config reinforce unique scope.

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?

Description implies batch submission for multiple tasks with shared endpoint and config, contrasting with single-task submit. However, it lacks explicit when-not-to-use or alternative references beyond the name.

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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