Get prioritized refactoring suggestions for code quality improvements.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | The code snippet to review for refactoring opportunities | |
| 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?
With no annotations, the description carries the burden. It indicates the tool returns suggestions rather than making changes, which is a useful behavioral trait, but it lacks details on processing, output format, or any caveats.
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, front-loaded sentence with no filler. It efficiently communicates the core purpose.
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?
The tool is simple (2 params, no output schema), but the description does not mention the output format or clarify that it is non-destructive, which could be important for an AI agent. It is adequate but has gaps.
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 100%, so both parameters are already documented. The description adds context about intent but no additional semantic details beyond the schema.
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 'Get prioritized refactoring suggestions for code quality improvements' clearly identifies the action (get suggestions), the resource (code refactoring), and a specific trait (prioritized). It is distinct from siblings like explain_code and analyze_complexity.
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 code quality improvements but does not explicitly state when to use it over alternatives, nor does it mention exclusions. Sibling tools exist, but no comparison is made.
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.