pyside6-mcp

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get_logs

Retrieve recent log messages from a PySide6 app's Python logging system, with timestamps, levels, logger names, and messages, to debug issues without modifying source code.

Instructions

Get the last n log messages captured from the app's Python logging system. Includes timestamp, level, logger name, and message.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_logs"
  }
}

Input Schema

Name	Required	Description	Default
`n`	No
`pid`	No	App pid from launch_app. Omit to target the last launched app. Required only when several apps are running and you need a specific one.

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 5, 2026

TDQS

B3.4/5.0

Behavior3/5

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

No annotations are provided, so the description carries the behavioral disclosure burden. It does disclose the data source ('app's Python logging system') and the included log fields. However, it does not describe ordering, truncation behavior, log retention, or whether logs are cleared between app launches.

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 two concise sentences with no filler. It front-loads the main action and then lists the returned fields, making it easy to scan and understand quickly.

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 log retrieval tool with an output schema, the description and input schema cover the core invocation needs: what data is returned, where it comes from, and how to target a specific app via pid. The main gap is the lack of guidance on choosing this over sibling tools, which is a usage-rule gap rather than a fundamental completeness gap.

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 50%; the pid parameter is well-documented in the schema, while n only has a default. The description adds meaning to n by saying 'last n log messages', clarifying it as a count. It does not explain edge cases like n<=0 or very large values, but overall the description and schema together give adequate parameter understanding.

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 names a specific verb and resource: getting the last n log messages from the app's Python logging system. It also lists the contained fields (timestamp, level, logger name, message), making the tool's purpose clear. It does not explicitly name or differentiate from sibling tools like get_app_output, but the 'Python logging system' phrasing provides reasonable distinction.

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?

The description gives no guidance about when to use this tool versus alternatives such as get_app_output or get_app_state. There are no explicit exclusions, prerequisites, or preference rules. Usage context is only implied by the tool name and description.

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