Generate a natural language explanation of a code snippet including structural analysis, features, and summary.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | The code snippet to explain | |
| language | No | Programming language (optional, auto-detected if omitted) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full behavioral disclosure burden. It states the output includes natural language explanation, structural analysis, and summary, which is useful, but it does not mention side effects, permissions, or error handling for invalid code. Given the likely read-only nature, this is a moderate gap.
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, concise sentence that front-loads the action and output. Every word contributes meaning, 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?
Given the tool's low complexity (2 parameters, no nested objects, no output schema), the description provides sufficient context for basic invocation. It covers the core purpose and output content, though it lacks explicit guidance on tool selection and leaves auto-detection of language to the schema. Overall, it is fairly complete for a simple 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?
Schema coverage is 100%, with clear descriptions for both parameters (code and language). The description adds no additional parameter semantics beyond what the schema already provides, so the baseline 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 resource ('code snippet'), and specifies the output scope ('structural analysis, features, and summary'). This clearly distinguishes it from siblings like generate_docstring or suggest_refactor, which have different output types.
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 usage for explaining code snippets but provides no explicit when-to-use or when-not-to-use guidance. It does not compare with sibling tools such as analyze_all or generate_docstring, leaving the agent without clear selection criteria.
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.