Generate JSDoc, Python docstring, Go godoc, or Rust doc comments for functions in the provided code.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | The code snippet containing functions to document | |
| language | No | Programming language (optional, auto-detected if omitted) |
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?
No annotations are provided, so the description carries full responsibility for disclosing behavior. It does not state whether the tool returns the generated docstring text as output, whether it modifies the input code, or how it handles multiple functions or invalid input. The auto-detection behavior mentioned in the schema is not reflected in the description.
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 a single, 15-word sentence that is front-loaded and contains no extraneous information. It efficiently conveys the core purpose and supported formats.
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?
With no output schema, the description should explain what the tool returns (e.g., the generated docstring text) but does not. It also omits mention of the optional language auto-detection behavior, leaving the tool's operational expectations incomplete for an agent.
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 input schema has 100% coverage with descriptions for both 'code' and 'language'. The description adds no additional parameter semantics beyond what the schema already provides, so the baseline score of 3 is appropriate.
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 uses a specific verb 'Generate' and clearly identifies the resource: doc comments for functions. It lists four specific output formats (JSDoc, Python docstring, Go godoc, Rust doc comments), distinguishing it from sibling tools like analyze_all, explain_code, or suggest_refactor.
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 implies the tool is for generating doc comments, but it does not explicitly state when to use it versus alternatives such as analyze_complexity or explain_code. No exclusionary guidance is provided; the usage context is inferred from the tool's name and purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct aspect of code analysis: language detection, explanation, docstring generation, complexity metrics, and refactoring suggestions. analyze_all is explicitly a combined wrapper, so agents won't confuse it with individual tools.
All tool names follow a consistent verb_noun pattern (e.g., detect_language, explain_code, generate_docstring). analyze_all uses the same verb-first structure, with 'all' as the object, preserving the convention.
Six tools is an ideal size for a code analysis server, covering the main analysis categories without bloat. Each tool provides a distinct value and the combined analyze_all tool adds convenience without unnecessary overhead.
The tool surface comprehensively covers the domain of code explanation and analysis: language detection, natural-language explanation, docstring generation, complexity assessment, and refactoring suggestions. The analyze_all tool ensures all features are accessible in one call, leaving no obvious gaps for common code-explanation workflows.