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Query a KiCad file's structure

query_design
Read-onlyIdempotent

Use to read a KiCad file as STRUCTURE rather than text: pass a file path from list_board_files and a select path of node names from the root, and get the matching nodes back as data. For example select ["kicad_pcb","layers"] for the layer stack, ["kicad_pcb","footprint"] for the footprints, ["kicad_sch","lib_symbols"] for symbol definitions, or query a .kicad_pro for design rules and net classes. Far cheaper and more reliable than reading a multi-megabyte board as raw text with read_file. For schematic CONNECTIVITY prefer read_schematic, which is already solved; use this for everything the netlist does not carry.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYesThe exact file path within the board, as returned by list_board_files. Do not guess.
boardYesA public board: handle/slug (e.g. alice/keezyboost40) or a boardrepo.com URL. Discover it with search_boards; do not invent one.
depthNoHow many levels of children to expand (default 2). Keep it small: a deep expansion of a large board is a large response.
cursorNoOpaque pagination cursor from a previous call; pass to fetch the next page of matches.
selectYesNode names from the root downward, e.g. ["kicad_pcb","layers"] or ["kicad_sch","lib_symbols","symbol"]. The first entry is the file's root node.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteNo
pathYes
selectYes
matchesYes
nextCursorYes
hasNextPageYes
totalMatchesYes

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds value by explaining that the tool interprets the file structurally, returns matching nodes as data, and is cheaper and more reliable than reading large board files as text. This gives a useful behavioral model beyond the annotations.

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 compact and well-structured: it states the core purpose first, then gives examples, then routes users away from sibling tools. Every sentence contributes useful decision-making or invocation guidance with no filler.

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 rich input schema, output schema, and strong annotations, the description covers the tool's role, usage context, alternatives, and example selects. Nothing critical is missing for an agent to decide when and how to invoke it.

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?

The input schema already documents all parameters at 100% coverage, so the baseline is 3. The description adds meaningful semantic context by telling users to obtain the file path from list_board_files and by giving realistic select-path examples for layers, footprints, symbols, and net classes.

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 reads a KiCad file as structured data rather than raw text, with a specific verb and resource. It provides multiple concrete examples for different file types, which makes its purpose immediately distinguishable from siblings like read_file and read_schematic.

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?

It gives explicit when-to-use guidance: when you need structure, not text, and when you already have a file path from list_board_files. It also names alternatives directly—read_file for raw text, read_schematic for connectivity—and says to prefer this when the netlist does not carry the information.

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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TDQS

A4.7/5.0
Disambiguation5/5

Each tool targets a distinct resource and action: board metadata, BOM, stored checks, file listing, fab profiles, structured queries, raw file contents, schematic connectivity, running checks, text search within a board, and board search across the corpus. Even similar pairs like get_checks/run_checks and search_board/search_boards are clearly separated by their descriptions and intended use.

Naming Consistency5/5

All tools follow a consistent snake_case verb_noun pattern, with get_, list_, read_, query_, run_, and search_ prefixes that accurately signal their behavior. There are no style mixes or ambiguous generic verbs.

Tool Count5/5

Eleven tools is well within the ideal range and each tool earns its place by covering a distinct aspect of board discovery, retrieval, and design inspection. The set feels complete without redundancy or bloat.

Completeness5/5

The tool surface covers the full read-side workflow: searching boards, fetching metadata and BOMs, inspecting schematic connectivity, reading raw or structured files, listing fabrication profiles, and retrieving or running DRC/ERC checks. No obvious gaps exist for a read-only public KiCad board repository browser.