The server provides a single well-structured tool (search_web) with a complete JSON Schema, explicit descriptions, and proper error handling for the core functionality. The tool name follows verb_noun convention and is clear. However, the server is narrowly scoped (1 tool only), lacks tool annotations (readOnlyHint, destructiveHint), and the implementation shows minimal composition patterns. The schema is properly declared with constraints (min/max for count and offset), and parameter descriptions are present. Error handling returns structured TextContent with actionable messages. The definitions are solid for a single-tool server, placing it in the B/B+ range.

• Add tool annotation to search_web: set readOnlyHint=true in the Tool definition to signal that the tool only reads data and has no side effects. This helps agents reason about safe retry behavior.
• Document the output schema explicitly in the tool description or as a structured comment. Example: 'Returns a dict with keys: results (list of {url, title, snippet}), total_results (int), offset (int)'. Alternatively, return a pydantic model that auto-documents via the schema.
• Enhance the empty query error message to include guidance: 'Query cannot be empty. Please provide a search term to proceed.' This follows the actionable-errors pattern.
• Update the tool description to mention pagination behavior: 'Supports pagination via count (1-20 per page) and offset parameters. Use count + offset to iterate through result pages.'
• Validate the count and offset parameters early. If count > 20 or < 1, return a clear error: 'Invalid count: must be between 1 and 20. Received: <value>.' This prevents wasted API calls and helps the LLM self-correct.
• Return a structured response object with typed fields (e.g., results: List[Dict], total_results: int) instead of raw JSON. This makes the output predictable for downstream agent reasoning.