Critical failures across all dimensions. Tool definitions are inferred from .mcp.json configuration only, no actual implementation code visible. No input schemas, parameter descriptions, or output documentation provided. Descriptions are minimal (10-50 chars) and lack actionable context. No error handling guidance. This is a wrapper/bootstrap kit, not a functional MCP server implementation.

• Add explicit input schemas to each tool in .mcp.json or implementation. Define parameter types, required fields, enums, and constraints. Example: get_minimal_context_tool should declare parameters like 'file_path' (string, required), 'depth' (integer, 1-10, default 3).
• Expand tool descriptions to 50-200 characters. Answer: What does it do? When should the LLM call it? What does it return? Example: 'Get minimal context from code-review-graph: returns the smallest set of code nodes needed to understand a change, useful for quick reviews of small PRs.'
• Rename 'query_graph_tool' to a verb_noun pattern that clarifies intent: 'search_graph_nodes', 'filter_graph_by_pattern', or 'traverse_graph_dependencies'. This helps LLMs disambiguate from other tools.
• Document output schemas for each tool. Specify returned fields, types, and structure. Example: get_review_context_tool returns {context: string, file_paths: string[], line_ranges: {start: int, end: int}[], confidence: float}.
• Add parameter descriptions to all tools. For each parameter, explain its purpose, valid values, and format. Example: 'depth (integer, 1-10): How many levels of dependencies to include. Default 3 for performance.'