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Glama
SorataYang

Qiao-MCP

by SorataYang

remove_elements

Destructive

Delete elements from a bridge structural model by ID, range (e.g., '1to10'), or all. Optionally remove nodes left unused after deletion for clean model updates.

Instructions

    Delete elements from the model (删除单元).

    Args:
        ids: Element ID(s) to delete. Supports int, list, or range string '1to10'.
             Leave empty to delete ALL elements.
             (单元编号,留空则删除全部单元)
        remove_free_nodes: Also delete nodes that become free after element deletion
                           (是否同时删除孤立节点,默认不删除)
        confirm_delete_all: MUST be set to true if ids is empty (deleting all elements).
                            (如果要删除所有单元,必须设为 true)

    CRITICAL LLM INSTRUCTION: Do NOT delete all elements autonomously to fix your own mistakes.
    You MUST explicitly ask the USER for permission before calling this tool with empty ids.
    (大模型绝对不可为了修复自己的错误而自行清空所有单元!必须先向用户询问并获得许可!)

    Example:
        remove_elements(ids="11to20")
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idsNo
remove_free_nodesNo
confirm_delete_allNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.3.1

TDQS

A4.7/5.0
Behavior5/5

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

The description goes well beyond the destructiveHint annotation by explaining that empty ids triggers total deletion, that confirm_delete_all is mandatory in that case, and that free nodes can optionally be removed. It also adds a critical rule prohibiting autonomous mass deletion without user permission.

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 front-loaded with the core action and organized into clear Args and warning sections. Some bilingual repetition adds length, but the critical safety instruction and example provide enough value to justify their inclusion.

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 a destructive three-parameter tool with no output schema, the description covers input formats, all parameters, the mandatory confirmation flag, and a worked example. Nothing essential to correctly invoking the tool is missing.

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?

Schema description coverage is 0%, but the description fully compensates by documenting all three parameters: ids supports int, list, or range string '1to10'; remove_free_nodes controls orphan node deletion; confirm_delete_all guards bulk deletion. This adds substantial meaning beyond the schema's minimal type/default information.

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?

Description states a specific action ('Delete elements from the model') on a clear resource, and the ids parameter clarifies scope including partial or full deletion. Although sibling tools like remove_nodes and remove_section exist, the tool's focus on 'elements' distinguishes it sufficiently.

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 clear context for when ids can be left empty and explicitly states that confirm_delete_all must be true. It includes a strong LLM-specific instruction to ask the user before deleting all elements, but does not discuss alternatives or exclusions versus other removal tools.

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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