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Kratos MCP Server

mdpa_set_elemental_data

Set or update elemental and conditional data rows in MDPA files by mapping IDs to values, or delete rows with remove_ids. Writes variables for Kratos simulations.

Instructions

Upsert 'Begin ElementalData ' (kind 'elemental') or 'Begin ConditionalData ' (kind 'conditional') rows: values maps element/condition ids to numbers, lists (written space separated) or raw strings such as "[3](0.0, 1.0, 0.0)" for vectors (kept verbatim). remove_ids deletes rows. Ids must exist; rows follow renumber/merge/delete automatically.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindYes
pathYes
outputNo
valuesNo
variableYes
overwriteNo
remove_idsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.9.0

TDQS

A3.7/5.0
Behavior4/5

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

With no annotations, the description carries the burden and does well: it discloses the upsert semantics, the exact value encoding (numbers, space-separated lists, raw strings like "[3](0.0, 1.0, 0.0)" kept verbatim), that remove_ids deletes rows, referential integrity ('ids must exist'), and cascading behavior (rows follow renumber/merge/delete automatically). It omits what 'overwrite' changes and whether a session/path must already be open.

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?

Three sentences, no filler, front-loaded with the core upsert action and block names. Encoding rules and the renumber/merge/delete note each earn their place by adding semantics the schema lacks.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 7-parameter mutation tool with no annotations, the description covers the data-encoding and referential aspects well, and the output schema handles return values. However, it is silent on the overwrite flag and the path/output parameters, which an agent needs to call this safely and correctly.

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 0%, so the description must compensate. It explains 'kind', 'variable' (via the block name), 'values' (multiple accepted forms), and 'remove_ids'. It leaves 'path', 'output', and critically 'overwrite' (default false) unexplained, so coverage of the 7 parameters is only partial.

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?

The description states a specific verb+resource: it upserts 'Begin ElementalData <variable>' or 'Begin ConditionalData <variable>' rows, and maps the 'kind' values (elemental/conditional) to those blocks. An agent can tell exactly what gets written. It stops short of naming sibling alternatives such as mdpa_set_nodal_data or mdpa_set_sub_model_part_data to distinguish scope.

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 rather than stated: the elemental-vs-conditional branch is explained, and 'Ids must exist' and 'remove_ids deletes rows' hint at preconditions. There is no explicit when-to-use/when-not framing and no pointer to the analogous nodal/sub-model-part setter tools, so the agent must infer selection.

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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