PeopleForce MCP demonstrates solid fundamentals: all 19 tools have clear verb-noun naming (list_*, find_*, get_*), complete input schemas with type definitions, and descriptions ranging 50 - 200 chars. Tool annotations (readOnlyHint, destructiveHint, openWorldHint) are present. However, output schemas are not documented, LLMs cannot predict response structure for downstream chaining. Parameter descriptions are minimal (e.g., 'Employee id' lacks context on format or range). Error handling is absent from visible code; no guidance on retryability or recovery. Pagination is well-designed (page/per_page with max 100), but no mention of total counts or cursors. The find_employee_by_email tool shows good composition (internal pagination), but most list_* tools are generic reference data with limited business value for agent reasoning.

• Document output schemas for all tools. For list_* tools, specify: { data: Array<{id, name, ...}>, pagination: {page, per_page, total} }. For get_* tools, list all returned fields and their types. This enables LLMs to plan downstream calls.
• Enhance parameter descriptions with format, range, and defaults. E.g., 'employee_id (integer, 1 - 999999): The numeric PeopleForce employee ID. Obtain via list_employees or find_employee_by_email.' and 'per_page (integer, 1 - 100, default 50): Number of results per page.'
• Add business context to reference-data tools. E.g., 'Call this to populate a dropdown of valid job levels before creating or updating an employee position.' This guides LLM selection.
• Implement error handling with recovery hints. Return structured errors: { error: 'not_found', message: 'Employee 12345 not found.', suggestion: 'Try search_employees or verify the ID.' }
• Extend pagination responses to include total count and has_more flag. E.g., { data: [...], pagination: { page: 1, per_page: 50, total: 237, has_more: true } }. This lets LLMs determine if more fetches are needed.