This server exposes a single tool 'search' with a non-standard implementation that simulates semantic search via word overlap rather than actual embedding. The tool definition itself has a description and parameter annotations visible in client.py, but critical quality gaps exist: (1) The input schema shows parameter names and types (query: string, documents: list, top_k: integer) but lacks proper JSON Schema structure with individual parameter descriptions embedded in the schema definition, what is visible appears to be a Python docstring interpretation rather than a formal MCP tool schema registration. (2) The tool description is present (50+ chars) but generic: 'Search documents with semantic ranking and grounded answer generation.' It does not explain when to use this vs other search patterns, what 'grounded' means, or how the output should be interpreted by an LLM. (3) Parameter descriptions are minimal: 'documents' is described as 'List of documents to search (dicts with text and optional title keys, or strings)', adequate but not LLM-optimized; 'top_k' lacks guidance on the impact of different values (3 vs 10 vs 100). (4) No output schema is documented, the return dict includes 'ranked_results' (list of dicts with rank, title, text[:100], embed_score) and 'generated_answer' (string), but there is no formal schema definition visible in the code. (5) The 'text' field is hardcoded to return only the first 100 characters (text[:100]), which truncates information and may confuse LLMs expecting full text. (6) No error handling is visible, what happens if documents is empty? If top_k > len(documents)? The code would silently return fewer results. (7) No validation of inputs, top_k could be negative or zero; query could be null. (8) The 'embed_score' algorithm is a trivial word-overlap heuristic, not actual semantic embedding, which is misrepresented by the tool description claiming 'semantic ranking.' This is a reliability and trust issue. (9) No explicit tool registration visible, the tool definition appears to be inferred from the method signature, not registered via an MCP ToolDefinition structure with proper schema. Code review shows example_usage.py demonstrates calling the client directly, not through an MCP protocol layer.

• Define the tool using formal MCP ToolDefinition with complete JSON Schema. Include type, description, enum/pattern constraints, and min/max for all parameters. Example: { 'query': { 'type': 'string', 'description': 'Search query (1-500 characters). Use natural language or keywords.', 'minLength': 1, 'maxLength': 500 }, 'documents': { 'type': 'array', 'description': 'List of documents to search (1-1000 items). Each document is a dict with required "text" (string, max 5000 chars) and optional "title" (string, max 200 chars).', 'minItems': 1, 'maxItems': 1000, 'items': { 'type': 'object', 'properties': { 'text': { 'type': 'string' }, 'title': { 'type': 'string' } }, 'required': ['text'] } }, 'top_k': { 'type': 'integer', 'description': 'Number of top results to return (1-100, default 3). Higher values increase latency and token cost.', 'minimum': 1, 'maximum': 100, 'default': 3 } }
• Rewrite tool description to be LLM-optimized (100-150 chars). Example: 'Search and rank documents by relevance to a query. Returns up to top_k ranked results with relevance scores (0-1) and a brief grounded answer snippet. Use this when you need to find specific documents or answers within a document collection.'
• Document the output schema explicitly. Example: '{ "ranked_results": [{ "rank": 1, "title": "string", "text": "string (full document, not truncated)", "embed_score": 0.0-1.0 }], "generated_answer": "string (80-200 chars, derived from top result)" }'
• Remove the text[:100] truncation or document it clearly. If truncation is necessary for performance, explain in the description and make it configurable via a parameter like 'snippet_length' (default 100, max 1000).