CallScope provides 6 well-named, verb-first tools with complete input schemas and clear descriptions. All tools follow the verb_noun pattern (analyze_project, search_functions, get_function, callers_of, call_path, generate_map). Descriptions are substantive (100-200 chars), explaining what each tool does and when to use it. Input schemas are properly typed with required fields and defaults. However, output schemas are not documented, LLMs cannot predict what fields to expect from responses. Error handling is absent from descriptions. The generate_map tool performs a write operation but lacks confirmation/dry-run guidance. Parameter descriptions are good but could specify constraints (e.g., 'limit' has no min/max bounds stated).

• Document output schemas for all 6 tools. Specify fields, types, and structure. E.g., analyze_project returns {stats: {function_count: int, module_count: int}, modules: [string]}. search_functions returns {results: [{function_id: string, name: string, module: string, line: int}], total: int}.
• Add min/max constraints to numeric parameters. E.g., 'limit: integer, 1 - 100 (default 25)' in search_functions. Prevents LLMs from passing 0 or 999999.
• Enhance generate_map with a dry_run parameter (boolean, default false). When true, return the output path without writing the file. Lets agents preview before committing.
• Add error recovery hints to tool descriptions. E.g., 'If path is invalid, ensure it is an absolute path to a Python project root. If function_id not found, try search_functions() first.'
• Expand callers_of description to clarify its role: 'Returns all functions that call the given function (reverse call graph edges). Use this to understand who depends on a function, or to trace back from a callee to its callers.'