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ads_write_many

Destructive

Write multiple PLC symbols in one atomic ADS sum-write after pre-checking gating, types, ranges, and constraints; if any check fails, nothing is written and all errors are listed.

Instructions

Write several symbols ({name: value}) together. Every symbol is gated (allow/deny list) and pre-checked (type, range, enum name, array/string length, CONSTANT, pointer/interface) BEFORE anything is written — one refusal → nothing is written and every problem is listed. Then ONE ADS sum-write: the runtime applies all values between the same two task cycles (method says if it had to fall back to one-by-one). Per-item before/written/read_back; applied/failed/not_attempted if the PLC rejects one.

Input Schema

NameRequiredDescriptionDefault
valuesYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.6.0

TDQS

A4.1/5.0
Behavior5/5

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

The description discloses behavior well beyond the annotations: pre-write gating (allow/deny list plus type/range/enum/length/CONSTANT/pointer checks), atomic all-or-nothing refusal with every problem listed, a single sum-write falling back to one-by-one, and per-item applied/failed/not_attempted reporting on PLC rejection. This is exactly the mutation-side context an agent needs and annotations do not provide.

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?

Three dense sentences, front-loaded with the core action, then validation semantics, then write/failure semantics. Every clause carries information, though the validation parenthetical is heavy and could be tighter.

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?

For an atomic batch-mutation tool with an output schema present, the description covers validation, atomicity, fallback, and partial-failure reporting, which is everything an agent needs to call and interpret it. Return-value detail is legitimately left to the output schema.

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?

Schema coverage is 0% for the single free-form 'values' object, so the description must carry the load. It supplies the {name: value} map shape and, via the validation list, hints that values may be scalars, enums, arrays/strings, or pointers, which is meaningful added semantics. It stops short of per-type syntax or size limits.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (write) and scope (several symbols together as a {name: value} map), which implicitly contrasts with the single-symbol ads_write/ads_read siblings. It does not name an alternative tool explicitly, so the differentiation is inferred rather than stated.

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

Usage Guidelines3/5

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

Usage is implied: use this for batched writes where you want all-or-nothing semantics, versus one-by-one. There is no explicit 'use X instead when Y' routing to ads_write, ads_write_raw, or ads_write_many's read counterpart, so the agent must infer the choice from context.

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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