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KonghuanSmart

calculate-mcp

batch_calc

Execute multiple calculation steps sequentially in one call, letting later steps reference earlier results and returning all outputs at once.

Instructions

Executes multiple calculation steps in one call and returns all results at once.

Steps run sequentially in array order. Each step is {"op": "", "args": {...}}, where "op" is any of: add, subtract, multiply, division, sum, modulo, floor, ceiling, round, mean, median, mode, min, max, sin, arcsin, cos, arccos, tan, arctan, radiansToDegrees, degreesToRadians, int_convert, bitwise, endian_swap, ieee754_convert, crypto_calc, data_codec.

Chaining: an argument value may be a reference to an earlier step's result: {"$step": } -> the whole result of that step {"$step": , "field": ""} -> a specific field of that step's object result (required when the result is an object, e.g. "resultHex", "decimal", "hex", "base64")

References only point to earlier steps (index < current). Returns a compact JSON array in step order, each item {step, op, status, result|error|reason}. A step is "skipped" only if a step it depends on failed; unrelated steps still run.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stepsYesOrdered calculation steps; later steps may reference earlier results

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It excels here: it describes sequential execution, failure propagation ('skipped only if a step it depends on failed; unrelated steps still run'), forward-only references, and the exact return structure. It does not cover authentication, rate limits, or resource constraints, but for a pure calculation tool these are likely irrelevant.

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 tight and front-loaded: first sentence states capability, then execution order, then step format, then chaining syntax, then return format. Every sentence earns its place, and no information is repeated unnecessarily.

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 complexity of a batch execution tool with chaining, the absence of an output schema, and no annotations, the description provides exactly what an agent needs: operation list, step structure, reference syntax, execution order, failure semantics, and return shape. It is complete enough to invoke correctly without external documentation.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents the single 'steps' parameter and its nested structure with enums. The description adds the crucial reference syntax ('{"$step": <index>}') and chaining rules beyond the schema, but does not provide operation-specific parameter details. Given the high schema coverage, a baseline 3 is appropriate.

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 states a specific verb+resource ('Executes multiple calculation steps in one call') and immediately positions itself against its many siblings by being a batch orchestrator, not a single operation. An agent can clearly distinguish this from add, subtract, etc.

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?

The description implicitly and explicitly guides when to use: 'Executes multiple calculation steps in one call and returns all results at once' makes batch use the clear condition, and the chaining mechanism defines exactly when references are appropriate. The sibling tools are single operations, so the batch tool's niche is obvious.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.