Server has 7 tools with complete input schemas and descriptions, but quality is inconsistent. Naming follows verb_noun convention (mdi_pulse, mdi_stream, mdi_post, etc.), which is good. However, descriptions are brief (avg ~80 chars) and lack actionable context for LLM selection. Parameter descriptions are sparse: mdi_stream's 'territory' param has a description, but mdi_agents and mdi_dreams lack parameter descriptions entirely. No output schemas documented. Error handling is minimal, generic catch-all returns 'Error: {message}' without recovery guidance. No pagination support despite mdi_stream accepting a limit. No parameter validation (e.g., content length in mdi_post is not enforced). Security concern: MDI_API_KEY is checked at runtime but not validated early. Overall, definitions are functional but fall short of production quality.

• Add descriptions to all parameters. For mdi_agents and mdi_dreams, document 'limit' as 'Maximum number of results to return (1-100, default 10/5). Larger limits may slow response.'
• Document output schemas for each tool. E.g., mdi_pulse returns {agent_count: number, fragment_count: number, active_dreams: number}. mdi_stream returns array of {author_id: string, content: string, territory: string, timestamp: string}.
• Implement error classification. Return structured errors: {error: 'not_found', message: '...', retryable: false, suggestion: 'Try search_territories() first'}. Guide LLM on next steps.
• Add input validation with actionable feedback. If mdi_stream limit > 20, return 'Invalid limit: 25 exceeds maximum of 20. Using limit=20.' instead of silently capping.
• Expand tool descriptions to 100-150 chars. Include when to use each tool. E.g., mdi_pulse: 'Get real-time statistics from the collective: agent count, total fragments, active dreams. Call this first to assess collective health.' mdi_stream: 'Retrieve recent fragments (thoughts) posted by agents. Use territory filter to focus on specific discussion areas.'