mcp-micropython-bridge

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micropython_read_stream

Read stream output from a connected MicroPython device for a set duration, stopping early on idle timeout or byte limit.

Instructions

接続中のデバイスから一定時間ストリーム出力を読み取る

Args: duration: 読み取りを続ける最大秒数 idle_timeout: この秒数だけ無通信なら早期終了 max_bytes: 読み取る最大バイト数。超えると truncated=True

Returns: ok: 成功 True stdout: 読み取ったテキスト truncated: max_bytes で打ち切られたら True bytes_read: 実際に読んだバイト数 error: エラー時のメッセージ

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": {
      "duration": "<duration>"
    },
    "name": "micropython_read_stream"
  }
}

Input Schema

Name	Required	Description	Default
`duration`	Yes
`max_bytes`	No
`idle_timeout`	No

Output Schema

Name	Required	Description	Default
`ok`	Yes
`error`	Yes
`stdout`	Yes
`truncated`	Yes
`bytes_read`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 13, 2026

TDQS

A3.6/5.0

Behavior4/5

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

With no annotations provided, the description carries the full burden and does add real behavioral context: duration caps the read, idle_timeout triggers early termination on inactivity, and exceeding max_bytes yields truncated=True. It doesn't state permissions or blocking behavior, but the timeout/truncation semantics are genuinely disclosed.

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?

Structured Args/Returns layout is front-loaded with the action sentence first, and each entry earns its place. The Returns block partly duplicates the output schema, but it still conveys useful semantics efficiently.

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?

Parameters are fully explained and the output fields are described, which is more than sufficient given an output schema exists. The only real omission is context on how this read tool relates to its numerous siblings.

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?

Schema description coverage is 0%, so the description must compensate, and it does: all three parameters are explained (duration=max seconds, idle_timeout=early-exit threshold, max_bytes=truncation limit) with the observable consequence of max_bytes spelled out. This meaningfully exceeds the bare schema.

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+resource: reading stream output from the connected device for a bounded time. This is clear, but it never distinguishes itself from the many read-oriented siblings (micropython_read_until, micropython_read_lines, micropython_read_file, micropython_exec), so an agent cannot tell which read tool applies without opening schemas.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use guidance and no exclusions. Given ~10 sibling read tools, the definition gives no signal for choosing read_stream over read_until or read_lines, leaving selection entirely to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.