muse_analyze_code

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

Since no annotations are provided, the description carries the full burden. It discloses that AST parsing is used, diagrams are generated by default, and AI analysis requires an API key. However, it does not detail potential side effects or output format, though the tool is likely read-only.

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 two sentences with no wasted words. It front-loads the core purpose and then expands on key features. Every sentence earns its place.

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 6 parameters, 100% schema coverage, no output schema, and no annotations, the description covers the tool's capabilities well, including the two modes (AST-only vs AI) and diagram generation. Minor missing detail on return format but otherwise complete.

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 baseline is 3. The description adds context like 'auto-detected' for language and 'AI-powered analysis for quality, security, and suggestions' for useAI, enriching the schema but not critically.

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 specific verbs and nouns: 'performs deep code analysis using AST parsing', 'extracts functions, classes, imports', 'generates Mermaid diagrams', and 'supports AI-powered analysis'. It clearly distinguishes itself from sibling tools like muse_git and muse_template, which have different purposes.

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 code analysis but does not explicitly state when to use this tool versus alternatives, nor does it provide when-not or exclude conditions. It lacks explicit guidance on prerequisites or contexts.

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