The 'search' tool has a single clear responsibility and action-verb naming (search_), which is good. However, the implementation reveals significant gaps in production-readiness: the description is present but generic (104 chars), the schema is basic with only one string parameter, output structure is undocumented, and error handling is entirely absent. The tool delegates to OpenAI's gpt-4o-search-preview model without validating inputs, handling failures, or guiding recovery. For a search tool that depends on an external API, this is a critical omission.

• Wrap the OpenAI API call in try/except blocks and categorize errors: network timeouts should be retryable, invalid API keys should halt initialization, rate limits should suggest backoff. Return structured error objects with actionable guidance (e.g., 'Search timed out after 10s. Try a shorter or more specific query.').
• Document the output schema formally. If the response is plain text, state: 'Returns a string containing search results and relevant web content.' If you plan to return structured data (sources, snippets, relevance scores), define the JSON schema.
• Add input validation: minimum length 1 char, maximum length 500 chars (or whatever OpenAI's gpt-4o-search-preview accepts). Validate before calling the API. Return clear validation errors: 'Query too long (512 chars). Maximum is 500. Shorten your query and try again.'
• Add an explicit timeout parameter (or default to 10 seconds) and wrap the openai_client.chat.completions.create() call with a timeout context manager. Return a retryable error if the timeout is exceeded.
• At server startup, validate that OPENAI_API_KEY is set and test the OpenAI client with a simple call. Log an error and refuse to start if initialization fails.
• Consider enriching the response with metadata: which model was used, how many tokens were consumed, what sources were cited. This helps agents understand the context and cost of the search.