All 6 tools have basic descriptions (10-80 chars) and input schemas with type definitions, but critical gaps undermine quality. Descriptions are generic and lack WHEN/WHY context. Parameters lack descriptions entirely, 'workspace' and 'context'/'issue'/'objective'/'diff'/'changes' have no guidance on format, constraints, or expected values. No output schemas documented. No error handling guidance. Tool names are action verbs (analyze, debug, plan, review_diff, document_repo, update_docs) but descriptions do not explain when to use each vs. similar tools. No pagination, no structured output guidance, no recovery hints. Schemas present but minimal, no enums, ranges, or validation rules. This is typical of early-stage tools that expose LLM capabilities without agent-centric design.

• Add descriptions to every parameter. For 'workspace': 'Absolute or relative path to the project root directory (e.g., /home/user/myproject or ./). Must contain a valid Rust/Python/JS project structure.' For 'context': 'Project context summary or file paths to analyze (e.g., src/main.rs, lib.rs, or a brief description of the codebase).'
• Document output schemas for each tool. E.g., analyze returns {analysis: string, issues: [{type: string, location: string, severity: string}], recommendations: [string]}. This enables agents to extract and chain results.
• Expand tool descriptions to 50-150 chars with WHEN/WHY guidance. E.g., 'Analyze a codebase to identify architectural issues, code smells, and improvement opportunities. Call this first to understand the project structure before planning changes.'
• Add error handling guidance. E.g., 'If the workspace path is invalid, returns error with available paths. If LLM service is unavailable, returns timeout error, retry after 30 seconds.'
• Add input constraints. E.g., workspace: 'Must be a valid directory path (max 500 chars)'; context: 'Max 10,000 chars; if longer, summarize or split into multiple calls'.