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apetta

Vibe Math MCP

by apetta

Batch Execute

batch_execute
Read-only

Execute multiple math operations in one request with automatic dependency chaining, reducing tokens and API calls for multi-step calculations like financial models and data pipelines.

Instructions

Execute multiple math operations in a single request with automatic dependency chaining.

USE THIS TOOL when you need 2+ calculations where outputs feed into inputs (bond pricing, statistical workflows, multi-step formulas). Don't make sequential individual tool calls.

Benefits: 90-95% token reduction, single API call, highly flexible workflows

Quick Start

Available tools (20): • Basic: calculate, percentage, round, convert_units • Arrays: array_operations, array_statistics, array_aggregate, array_transform • Statistics: statistics, pivot_table, correlation • Financial: financial_calcs, compound_interest, perpetuity • Linear Algebra: matrix_operations, solve_linear_system, matrix_decomposition • Calculus: derivative, integral, limits_series

Result referencing:

Pass $op_id.result directly in any parameter:

  • $op_id.result - Use output from prior operation

  • $op_id.result[0] - Array indexing

  • $op_id.metadata.field - Nested fields

Example: "payment": "$coupon.result" or "variables": {"x": "$op1.result"}

Example - Bond valuation:

{
  "operations": [
    {"id": "coupon", "tool": "calculate",
     "context": "Calculate annual coupon payment",
     "arguments": {"expression": "principal * 0.04", "variables": {"principal": 8306623.86}}},
    {"id": "fv", "tool": "financial_calcs",
     "context": "Future value of coupon payments",
     "arguments": {"calculation": "fv", "rate": 0.04, "periods": 10,
                   "payment": "$coupon.result", "present_value": 0}},
    {"id": "total", "tool": "calculate",
     "context": "Total bond maturity value",
     "arguments": {"expression": "fv + principal",
                   "variables": {"fv": "$fv.result", "principal": 8306623.86}}}
  ],
  "execution_mode": "auto",
  "output_mode": "minimal",
  "context": "Bond A 10-year valuation"
}

When to Use

✅ Multi-step calculations (financial models, statistics, transformations) ✅ Data pipelines where step N needs output from step N-1 ✅ Any workflow requiring 2+ operations from the tools above

❌ Single standalone calculation ❌ Need to inspect/validate intermediate results before proceeding

Execution Modes

  • auto (recommended): DAG-based optimization, parallel where possible

  • sequential: Strict order

  • parallel: All concurrent (only if truly independent)

Output Modes

  • full: Complete metadata (default)

  • compact: Remove nulls/whitespace

  • minimal: Basic operation objects with values

  • value: Flat {id: value} map (~90% smaller) - use this for most cases

  • final: Sequential chains only, returns terminal result (~95% smaller)

Structure

Each operation:

  • tool: Tool name (required)

  • arguments: Tool parameters (required)

  • id: Unique identifier (auto-generated if omitted)

  • context: Optional label for this operation

Batch-level context parameter labels entire workflow across all output modes.

Response includes: per-operation status, result/error, execution_time_ms, dependency wave, summary stats.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contextNoOptional annotation to label this calculation (e.g., 'Bond A PV', 'Q2 revenue'). Appears in results for easy identification.
output_modeNoOutput format: full (default), compact, minimal, value, or final. See batch_execute tool for details.full
operationsYesList of operations to execute. Each operation MUST include: tool (name), arguments (dict). Optional: id (UUID/string), context, label, timeout_ms (int)
execution_modeNoExecution strategy: sequential (order), parallel (concurrent), auto (DAG-based)auto
max_concurrentNoMaximum concurrent operations (applies to parallel/auto modes)
stop_on_errorNoWhether to stop execution on first error. If False, independent operations continue even if others fail.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Annotations include readOnlyHint=true, and the description aligns by focusing on computation without side effects. The description extensively covers execution modes, output modes, error handling, dependency chaining, and response structure, adding significant context beyond annotations.

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 well-structured with sections, bullet points, and a code example, making it easy to navigate. However, it is somewhat verbose; a few sentences could be tightened without losing clarity, especially in the Quick Start listing all 20 tools.

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 complexity (6 parameters, 20 sub-tools, dependency chaining, multiple modes), the description covers purpose, usage, structure, parameter details, examples, and response format, leaving no gaps for an AI agent to invoke the tool correctly.

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?

Input schema has 100% description coverage, but the description adds substantial meaning: it explains execution_mode options, output_mode formats with sizing, how to reference results ($op_id.result), and batch-level context. The example further clarifies parameter usage.

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 executes multiple math operations in a single request with dependency chaining, distinguishing it from sequential individual tool calls. The verb 'execute' and resource 'multiple math operations' are specific, and it differentiates from sibling tools by emphasizing batching.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit 'When to Use' section with checkmarks for multi-step calculations and crosses for single calculations. It also mentions token reduction benefits and references sibling tools in the Quick Start, providing clear guidance on appropriate contexts.

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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