This server has a single tool 'recommend_tier' with a very basic implementation. The tool has a reasonable name following verb_noun pattern, but critical definition quality issues severely limit its value. The input schema is present with proper types and descriptions, but the tool description is generic and lacks critical context about use cases, return value structure, and when to invoke it. The source code shows no MCP server registration pattern, only a client class with a Python method. No evidence of MCP protocol implementation, tool registration, or server startup. The tool appears to be a simple utility function, not a properly registered MCP tool. Additionally, the output schema is not documented in the tool definition, callers have no visibility into the structure of the response before invocation.

• Implement this as an MCP server using the official Python SDK (mcp package). Currently, only a client class exists, no server registration, no tool handler, no transport.
• Expand tool description to 100 - 200 characters explaining WHAT it does (recommends a pricing tier), WHEN to call it (when evaluating customer usage against pricing models), and WHAT it returns (a tier name and base monthly price). Example: 'Recommends the appropriate pricing tier based on customer API query volume and seat count. Returns tier name (Free/Starter/Professional/Enterprise) and monthly base price. Use when evaluating customer costs or selecting tier during onboarding.'
• Document the output schema explicitly: { 'recommended_tier': string enum (Free|Starter|Professional|Enterprise), 'monthly_base_price': number (currency in USD) }.
• Add minimum/maximum constraints to integer parameters. Set api_queries: minimum 0, maximum 1000000 (or domain-appropriate max). Set user_seats: minimum 1, maximum 10000. Document in parameter descriptions.
• Add a discovery tool or documentation tool that lists pricing tiers, thresholds, and exact pricing. This enables LLMs to explain recommendations and guide users. E.g., 'Call list_pricing_tiers to see all tiers and thresholds before recommending.'
• Implement error handling for edge cases: What if both api_queries and user_seats are at tier boundaries? Document the precedence rule (OR logic: max of either triggers a higher tier). Return clear error for negative inputs with guidance: 'Invalid api_queries: must be >= 0. Received -100.'