This server has critical structural issues. Tool names use a verbose namespace prefix (mcp__plugin_kimi_companion__) rather than action verbs, making them difficult for LLMs to parse intent. Descriptions are present but generic (10-50 chars), well below the 50-200 char baseline for LLM optimization. Input schemas are visible in the tool list but lack critical details: no enum constraints for preset/action/window/scope/group_by parameters, no min/max for context_bytes, no format specifications. Parameter descriptions are minimal. Output schemas are completely undocumented, LLMs cannot plan downstream calls. Error handling and recovery guidance are absent. The tool set shows poor composition: session management, status polling, result retrieval, and cleanup are separate tools when they could be unified into a single job lifecycle interface.

• Rename all tools to verb_noun pattern without namespace prefix: ask_kimi, review_code, explore_codebase, plan_implementation, manage_session, check_job_status, get_job_result, cancel_job, cleanup_job, query_usage, list_models, configure_plugin, initialize_plugin.
• Expand all descriptions to 80-150 chars. Example: 'Ask Kimi a question about the codebase. Returns a job_id for async processing. Use check_job_status to poll for completion, then get_job_result to retrieve the answer. Supports model profiles: fast, stable, deep, large-context.'
• Add enum constraints to all multi-value parameters. For review: preset enum [correctness, security, performance, api, tests]. For session: action enum [list, new, resume, fork]. For usage: window enum [today, 24h, 7d, 30d, all], scope enum [repo, all], group_by enum [day, model, kind, outcome].
• Document output schemas for all tools. Example for ask: {job_id: string, status: 'queued'|'running'|'finished'|'failed', created_at: ISO8601}. Example for result: {job_id: string, output: string, error?: string, duration_ms: number}.
• Consolidate job lifecycle into a single tool or provide a unified job_query(job_id, action='status'|'result'|'cancel'|'cleanup') to reduce LLM orchestration overhead and prevent polling errors.