fritzing-mcp

Fritzing Create Part

fritzing_create_part

Generate a custom Fritzing part (.fzp plus four view SVGs) from a JSON spec, optionally installing it into local_parts for the app to discover on restart.

Instructions

Generate a custom part (fzp + 4 view SVGs) from a JSON spec.

Custom parts must have all 4 views; restart Fritzing after --install (parts are scanned at startup).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "spec_path": "<spec_path>"
    },
    "name": "fritzing_create_part"
  }
}

Input Schema

Name	Required	Description
`out`	No	output directory for the generated fzp+SVGs
`install`	No	install into local_parts so the app discovers it
`spec_path`	Yes	path to a part spec JSON (schema in the fzkit docs)

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 13, 2026

TDQS

A3.6/5.0

Behavior3/5

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

With no annotations, the description must carry the behavioral load, and it does disclose the two most important traits: the exact output artifacts and the restart/scanned-at-startup caveat for install. It omits matters like whether existing parts are overwritten, error behavior on a malformed spec, or permission requirements, so it is useful but not complete.

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?

Two sentences, zero filler, with the core action front-loaded and the caveat second. Every clause earns its place.

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

Completeness4/5

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

An output schema exists so return values need not be explained, and the description covers the generation action plus the key install lifecycle gotcha. It is close to sufficient for a 3-parameter generation tool, only lacking conflict/overwrite semantics.

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 100%, so all three parameters (spec_path, out, install) are already documented in the schema; baseline is 3. The description adds only marginal meaning, hinting at the --install flow and referencing the JSON spec the schema already points to.

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 precise verb and resource ('Generate a custom part') and enumerates the concrete artifacts produced (fzp + 4 view SVGs) from a JSON spec. This clearly separates it from siblings like fritzing_validate_part or fritzing_place_part, though it never names an alternative explicitly.

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?

The second sentence gives a real lifecycle rule (all 4 views required; restart Fritzing after --install because parts are scanned at startup), which is actionable usage context. However, it never says when to reach for this tool versus validate_part or set_properties, so guidance is implied rather than explicit.

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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