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thebtf

netcoredbg-mcp

by thebtf

ui_select_items

Selects items by index in list or grid controls, supporting both replace and add selection modes. Handles virtualized lists and works with FlaUI and pywinauto backends.

Instructions

Select items by index in a list/grid control (DataGrid, ListView, ListBox).

With FlaUI backend: uses SelectionItemPattern (reliable for virtualized lists). With pywinauto backend: two strategies (tries both):

  1. UIA SelectionItemPattern — works for non-virtualized lists

  2. Coordinate click fallback — clicks items using cached rectangles (Ctrl+click for multi-select, plain click for first item)

For WPF virtualized lists (VirtualizingStackPanel), strategy 1 may fail because off-screen items don't have UI containers. Strategy 2 uses visible item coordinates from the cache. FlaUI backend handles this natively.

Args: automation_id: AutomationId of the list/grid control indices: List of 0-based item indices to select mode: "replace" (clear existing, select these) or "add" (add to existing selection)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
automation_idYes
indicesYes
modeNoreplace
Behavior5/5

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

The description thoroughly covers behavioral details: two backend strategies with fallback mechanisms, multi-select behavior (Ctrl+click), and limitations with virtualized lists. This goes well beyond the sparse annotation (only openWorldHint: false), providing the agent with essential operational knowledge.

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 a clear intro, backend details, and args list. It is front-loaded with the core purpose. However, it is somewhat lengthy; some backend implementation details could be shortened without losing essential guidance for the agent.

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?

Given the absence of an output schema, the description should mention the return value or outcome (e.g., success/failure, selected items). It also lacks error handling or prerequisite conditions (e.g., control must exist). This leaves an incomplete picture for the agent, though the behavioral details are strong.

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?

With 0% schema description coverage, the description adds meaning by explaining the three parameters (automation_id, indices, mode) in an 'Args' section. It clarifies that indices are 0-based and mode options are 'replace' or 'add', though it does not enumerate all possible mode values or provide additional constraints beyond the schema.

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 the tool's purpose: 'Select items by index in a list/grid control (DataGrid, ListView, ListBox).' This is a specific verb+resource pair, and it distinguishes itself from sibling tools like ui_get_selected_item which only retrieves selection, not modifies it.

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?

The description provides detailed backend-specific strategies and addresses virtualized list edge cases. It implies usage context (e.g., for reliable selection in virtualized lists, FlaUI backend is preferred) but does not explicitly state when not to use it or name alternatives for different selection needs.

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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