Instantly transform Python source code into clean, structured Markdown documentation. This tool parses your code's Abstract Syntax Tree (AST) to automatically extract module docstrings, class definitions, methods, and function signatures into a readable format. (reference price: $0.0000 per call)
| Name | Required | Description | Default |
|---|---|---|---|
| payload | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are absent, so the description must fully disclose behavioral traits. It mentions it parses AST to extract modules, classes, methods, and signatures, but does not disclose important details like whether the tool modifies files, requires authentication, rate limits, or if it overwrites existing documentation. The price note ('$0.0000 per call') suggests no cost, but overall transparency is limited.
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 description is concise at two sentences, front-loading the core purpose. The first sentence effectively states the action, and the second explains the mechanism. The price note, while minor, is arguably extraneous and could be removed to save space, but does not detract significantly.
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?
Given the tool has no annotations, poor parameter semantics (0% coverage), and no guidance on usage, the description is incomplete. While it mentions AST parsing, it omits crucial context like what input format the payload expects (code vs. file path), whether it writes output to a file, and what exactly happens if the payload is empty/null. An output schema exists but doesn't compensate for the missing behavior and input details.
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%, meaning the input schema doesn't describe parameters meaningfully. The description adds no details about the 'payload' parameter—what it should contain, its format (e.g., file path or code string), or whether it's optional (defaults to null). This is insufficient for a single-parameter tool.
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 transforms Python source code into Markdown documentation, specifying the verb ('transform'), resource ('Python source code'), and output ('structured Markdown documentation'). It distinguishes itself from sibling tools, which primarily involve codebase management or process automation, by focusing on documentation generation.
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 lacks guidance on when to use this tool versus alternatives. It does not specify prerequisites (e.g., that Python code must be provided), nor does it mention when not to use it. With no explicit when/when-not criteria, the agent must infer usage solely from the tool's purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.