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ReyemTech

mcp-canada

by ReyemTech

execute_batch

Execute multiple tool calls in parallel and aggregate results. Accepts a plan output or list of calls, with per-step error isolation.

Instructions

Execute multiple tool calls in parallel and return aggregated results.

Accepts either a plan_query output (dict with 'steps' key) or a raw list of tool call objects. Runs all valid steps in parallel using asyncio.gather with per-step error isolation — one failed step does not cancel others.

Use for: running multiple tool calls at once, executing a plan from plan_query, batch queries across multiple APIs, parallel data fetching, multi-source aggregation.

Keywords: batch, execute, parallel, multiple tools, run plan, aggregate, multi-step, concurrent, simultaneous, gather, dispatch, bulk, workflow

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
callsYes
langNoen

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

Discloses key behaviors: uses asyncio.gather for parallel execution, per-step error isolation (one failure doesn't cancel others), and accepts specific input formats. With no annotations, this adequately reveals the execution model.

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?

Efficiently structured: purpose first, then behavior, followed by use cases and keywords. Every sentence adds value without redundancy.

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?

Covers input format, execution model, error isolation, and use cases. With an output schema present, the return values are implicitly documented. Could add timeout details but overall comprehensive for the tool's complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Despite 0% schema coverage, description adds meaning by explaining the `calls` parameter accepts either a plan_query output or raw list of tool call objects. The `lang` parameter is an enum with default, and its description is not needed beyond schema.

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 'Execute multiple tool calls in parallel and return aggregated results,' effectively distinguishing it from siblings like call_tool (single call) and plan_query (generates plans without execution).

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?

Provides explicit use cases like 'running multiple tool calls at once, executing a plan from plan_query,' offering clear guidance on when to use. Could improve by mentioning when not to use, but positive guidance is strong.

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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