Critical failures across all dimensions. Tool definitions are inferred from test scripts and server code rather than explicitly visible in schema registration. No input schemas are visible in the provided source. Descriptions exist but are generic and lack LLM-optimization guidance. The server appears to be a Cloudflare Worker (wrangler config) but actual tool registration code is incomplete in the provided excerpt. Cannot verify parameter types, validation, or output schemas. The blind_tool_test.py script tests tool selection behavior but does not reveal the actual schema definitions required for scoring.

• Provide complete src/server.ts with explicit tool registration including full input schemas (JSON Schema with type, description, enum/pattern constraints for each parameter).
• Expand tool descriptions to 50-200 characters following LLM-optimization guidance: state WHAT the tool does, WHEN to use it, and any prerequisites. Example: 'Queries joined public records for Twin Cities (Minneapolis-St. Paul). Use this to find commercial property sales, permits, or business registrations. Requires a city/region name.'
• Add parameter descriptions for every input. For example, if data_source_atlas accepts 'city' and 'region', describe: 'city (string): The city name (e.g., Minneapolis, Denver). region (string, optional): State or metro area code (e.g., MN, CO).'
• Document output schemas for each tool. Example for twin_cities_records: 'Returns array of records with fields: record_id (string), property_address (string), sale_price (number), sale_date (ISO 8601), record_type (enum: commercial_sale|permit|registration).'
• Add error handling guidance to descriptions. Example: 'If city is not in Twin Cities, returns error with available cities. Call data_source_atlas first to verify coverage.'