Critical gaps across all dimensions. The mcp.json file provides tool names and brief descriptions but NO input schemas, NO parameter definitions, NO output schemas, and NO error handling guidance. Both tools lack the structured definitions required for reliable LLM invocation. Descriptions are present but minimal (34-56 chars, below the 50-200 char baseline for LLM-optimized docs). No evidence of parameter types, constraints, or validation. This is a skeleton definition that cannot support safe agent composition.

• Add complete JSON Schema input definitions for both tools. For people_search: document job_title, company, location, industry, seniority as parameters with types (string/enum), descriptions, and constraints (e.g., max 100 results, pagination support).
• Expand tool descriptions to 100-150 chars. Example: 'Search 130M+ B2B contacts by job title, company, location, industry, and seniority. Returns contact IDs, names, emails, and company info. Use this to find prospects; then call contact_enrich to get verified phone numbers.'
• Document output schemas for both tools. Specify: what fields are returned (contact_id, name, email, company, job_title, etc.), data types, and which fields are required vs optional. Include pagination info (total_count, next_cursor).
• Add parameter descriptions. For people_search: 'job_title (string): Job title to search for, e.g. "Sales Manager". company (string): Company name filter. location (string): Geographic location (city, state, or country). limit (integer, 1-100): Max results to return, default 20.'
• Implement error handling with recovery guidance. Example: 'No contacts found matching criteria. Try broadening the search (remove location filter, use partial job title). Or call contact_enrich with a known LinkedIn URL to verify a specific contact.'