analyze_file

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

With no annotations provided, the description carries full burden. It discloses critical behavioral traits: Claude never sees file contents (privacy), reads filePath and optionally includeContext files, makes one LLM call, and is read-only. It also explains the verbatim short-circuit behavior and the return shape, exceeding typical transparency.

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 lengthy but every sentence adds value. It front-loads the core purpose, then distinguishes alternatives, then explains the short-circuit edge case, then lists the return shape. No filler or redundancy; the structure guides the reader from general to specific details.

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?

No output schema exists, so the description must explain return values—it does, listing all fields in the return object. It also covers the verbatim short-circuit response variant and the behavior around file reading. Given the tool's complexity (nested options, multiple backends), the description provides comprehensive context for an agent to use it correctly.

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 67% (below the 80% threshold), so description must compensate. It adds meaning beyond the schema, e.g., clarifying that includeContext files are read and that question drives the analysis. It also explains that backend selection is influenced by analysisType, and describes the output fields like tokens_saved, which are not in the input 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 clearly states the tool's function: 'Read ONE file and answer a question about it using a local or cloud LLM.' It also distinguishes from siblings by explicitly naming alternatives (batch_analyze for many files, explore for codebase search) and describing the verbatim short-circuit for line-range questions.

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?

Provides explicit usage criteria: 'Use when you have one specific file and a specific question (security check, bug hunt, architectural concern).' It also gives clear alternatives: 'For the same question across many files (glob patterns), use batch_analyze. For a natural-language search across the codebase with no specific file in mind, use explore.' The line-range short-circuit condition is also explained.

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