analyze_python_ast

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

The readOnlyHint annotation already covers safety. The description adds behavioral detail by describing the return format, the per-hit fields (line, kind, value, extra, function, confidence), and the truncation flag. It doesn't contradict annotations.

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 well-structured: a one-sentence core value proposition, a bulleted list of search targets, and a compact return schema. Each sentence contributes new information, making it concise despite its length.

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 moderately complex with 8 parameters and no output schema, but the description compensates by specifying the JSON return structure and per-hit metadata. It does not cover edge cases like duplicate hits or performance, but the essential information for correct invocation and result interpretation is present.

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?

With 100% schema coverage, the baseline is 3. The description adds semantic meaning for each parameter type, especially field_names ('捕获 obj.field / obj['field'] / obj.get('field')') and string_values, and notes that multiple search types can be combined—a behavior not detailed in 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 clearly identifies the tool as performing AST-level precise analysis of Python code ('对 Python 代码做 AST 级别精确分析') and differentiates it from grep ('比 grep 更准确'). It enumerates five distinct search targets (symbols, field_names, string_values, call_names, import_names), which distinguishes it from siblings like scan_patterns.

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?

It explicitly states that the tool is suitable for scenarios needing precise context ('适合需要精确上下文的场景') and claims better accuracy than grep, giving an implicit comparison to alternatives. However, it does not name sibling tools or state when not to use it, so it stops short of full exclusion guidance.

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