Praxia MCP server has significant definition quality gaps. Tool names lack clear action verbs (skill_*, flow_* are templated placeholders, not concrete verbs). Descriptions are present but generic and lack LLM-optimized guidance on WHEN to use each tool vs alternatives. Parameter descriptions are minimal or missing context (e.g., 'user input / task description' for skill_* is vague; 'Flow inputs (key/value)' lacks structure). Output schemas are not documented anywhere in the provided code. Error handling and recovery guidance are absent. The autonomous_agent tool conflates multiple concerns (search + run skills + pull connectors) into one tool, violating single-responsibility. No evidence of input validation, rate limiting, or permission gates.

• Replace templated tool names (skill_*, flow_*) with concrete action verbs. Register each skill and flow as a distinct tool with a specific name: 'run_skill_hr_recruiting', 'execute_flow_incident_response', etc. This enables LLMs to select the right tool by name alone.
• Document output schemas for all tools. For search_memory, specify: {results: [{id, content, relevance_score, source}], total_count, next_cursor}. For autonomous_agent, specify: {status, result, steps_taken, errors}. For export_as, specify: {format, content_url, file_size}.
• Expand parameter descriptions with format, constraints, and examples. E.g., 'user_id (string, required): UUID of the user. Example: 550e8400-e29b-41d4-a716-446655440000'. 'query (string, required): Search terms (1-500 chars). Supports AND/OR operators.'
• Add error handling guidance to tool descriptions. E.g., 'search_memory returns 404 if user_id not found, call search_users() first. Returns 400 if query exceeds 500 chars, truncate and retry.'
• Split autonomous_agent into three focused tools: search_memory (already exists), run_skill (execute a named skill with inputs), and query_connector (fetch data from a specific connector). This enables the LLM to compose these independently and retry individual steps.