Evaluate Python expressions on MicroPython boards to test code or check device state, returning the result as a string.
MicroPython ボードで式を評価し、結果を文字列で返す。
Args: expression: 評価する Python 式 (例: "1 + 1", "machine.freq()")
Returns: ok: 評価に成功したら True result: 評価結果の文字列表現 error: エラー時のメッセージ。成功時は None
| Name | Required | Description | Default |
|---|---|---|---|
| expression | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| ok | Yes | ||
| error | Yes | ||
| result | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It discloses the success/error return shape, but does not explain side effects, safety constraints, timeout behavior, connection requirements, or what happens if the evaluated expression mutates board state.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The definition is compact and front-loads the purpose, followed by structured Args and Returns sections. The Returns section duplicates information already present in the output schema, which is a minor inefficiency, but the text remains clear and appropriately sized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with one parameter and an output schema, and the description covers purpose, argument meaning, and return fields. However, with no annotations and no usage guidelines or safety context, an agent lacks guidance on when to choose this tool over siblings and what behavioral risks exist.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 for the single parameter. It explains that 'expression' is the Python expression to evaluate and provides examples such as '1 + 1' and 'machine.freq()', adding useful meaning beyond the schema's bare type and title.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific verb and resource: 'evaluate an expression on a MicroPython board and return the result as a string.' It distinguishes the operation from general execution in spirit, but does not explicitly name or differentiate itself from sibling tools such as micropython_exec.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like micropython_exec or other execution-related siblings. It only documents the argument and return fields, leaving context 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.