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CadenceAI MCP

CadenceAI MCP

by chaosAIman
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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parse_brd_file

Extracts readable string fragments from .brd files to supply lightweight PCB context—layers, components, nets, rules—for AI summaries; not a full Allegro DB parser.

Instructions

从 .brd 文件中提取可读字符串片段,作为 PCB 设计的轻量级上下文(层、器件、网络、规则等)。适合作为 AI 摘要的输入,不能替代完整 Allegro DB 解析。

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": {
      "brd_path": "<brd_path>"
    },
    "name": "parse_brd_file"
  }
}

Input Schema

NameRequiredDescriptionDefault
brd_pathYes.brd 文件绝对路径。
max_bytesNo读取文件前多少字节做文本扫描,默认 1MB。

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

No annotations exist, so the description carries the full disclosure burden. It usefully signals that output is a lossy, lightweight extraction rather than a complete parse, but says nothing about failure modes, encoding, or how the string scan behaves on truncated input.

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?

Two compact sentences with no filler; the core purpose is front-loaded and the limitation follows. Efficient and appropriately sized.

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?

For a read-only parsing tool with no output schema, the description conveys what is extracted and the key caveat that it is lossy. It could say more about the shape of the returned fragments, but nothing essential to correct invocation is missing.

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 coverage is 100%, so brd_path and max_bytes are already documented in the schema. The description's mention of layers/components/nets is about extracted content, not about parameter meaning, so it adds nothing beyond the structured fields. Baseline 3 applies.

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 (提取/extract) and resource (.brd file), and enumerates the payload (layers, components, nets, rules). It distinguishes itself from parse_circuit_dsl by resource type, though it never names that sibling explicitly.

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?

It gives a clear intended context (input for AI summarization) and a boundary condition ('does not replace full Allegro DB parsing'), which functions as a when-not. It stops short of naming alternative tools to pick instead when a full parse is needed.

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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