python_to_txt

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

Discloses key behaviors: recursive scanning, skipping .venv/__pycache__/.git, respecting .gitignore, output file name, and format markers. However, it claims 'no parameters' while the schema includes an optional 'args' parameter, a slight inconsistency that reduces transparency.

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?

Compact and well-structured: the description front-loads the core action, then format, use cases, and exclusions. Every sentence adds value, with no redundancy or filler.

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?

For a simple tool with one optional parameter and no output schema, the description is comprehensive. It covers input scope, processing behavior, output format, excluded directories, and applicable use cases, making it sufficient for an agent to understand and invoke the tool.

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?

The description says no parameters are needed and defaults to the current directory, which clarifies usage, but it doesn't explain the existing 'args' field. With 0% schema coverage, the description could better compensate by addressing what the parameter might do or why it exists.

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 the tool's function: scanning .py files recursively, extracting code, and merging into a text file. It distinguishes from sibling tools like xlsx_to_txt by explicitly targeting Python files and providing output format details.

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?

Lists specific use cases (code review, AI context input, archive) but doesn't explicitly exclude alternatives or mention when not to use this tool. It provides clear context for when to use it, though it lacks a comparison to sibling tools.

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