Critical failures across all dimensions. The single tool 'execute_skill_action' has a vague, generic name that violates verb_noun conventions. Description is 104 chars but lacks WHEN/WHY context for LLM selection. Input schema exists but parameters lack type definitions (target_company_domain and prospect_title_filter are both strings with minimal constraint info). No output schema is documented, the response structure is inferred from client.py mock data, not formally declared. No error handling, validation guidance, or recovery paths. The server itself is a stub that prints JSON and exits; it does not actually implement MCP protocol message handling, tool invocation, or request/response cycles.

• Rename tool to 'enrich_b2b_prospects' or 'search_decision_makers', use a specific action verb that conveys intent without reading the description.
• Document the complete output schema with all fields (campaign_id, verified_decision_makers_found, waterfall_providers_queried, work_email_deliverability_rate_pct, hyper_personalized_outbound_pitch_drafted, crm_hubspot_salesforce_synced), their types, and what each represents.
• Add parameter constraints: target_company_domain should specify format (e.g., 'domain name without protocol, e.g. acmecorp.io'); prospect_title_filter should list valid job titles or patterns (e.g., 'C-level, VP, Director, Manager titles').
• Expand description to 150 - 250 chars explaining WHEN to use this tool (e.g., 'Use this to find and enrich decision makers at a target company for GTM outreach. Returns verified email addresses, job titles, and LinkedIn profiles.').
• Implement actual MCP protocol message handling in mcp_server.py: parse JSON-RPC requests, dispatch tool calls, validate inputs, catch errors, and return structured responses per the MCP spec.