Server defines 4 tools with basic schemas and descriptions, but exhibits significant quality gaps. Tool naming is inconsistent (search/fetch vs ref_search_documentation/ref_read_url), suggesting client-specific branching rather than clean composition. Descriptions are present but generic (avg ~120 chars, below 194-char baseline). Parameter descriptions lack detail on format/constraints. No output schema documentation. Error handling is minimal, no recovery guidance or actionable error messages. The dual naming pattern indicates the server conflates tool identity with client compatibility, violating single-responsibility principle.

• Consolidate to a single canonical tool pair (recommend 'search_documentation' + 'read_url'). Use server-side logic to adapt response format for OpenAI compatibility without duplicating tool definitions.
• Expand parameter descriptions with format constraints: 'query: string, 1-500 chars, should include language/framework names. Include ref_src=private to search private docs.' and 'url: valid HTTP/HTTPS URL from search_documentation results.'
• Document output schemas for both search and read tools. E.g., search returns [{id, title, url, snippet, source}], read returns {content: string, url, title, metadata}. Include pagination info if applicable.
• Add error handling with recovery guidance. E.g., 'API rate limited, retry in 60s' or 'Invalid URL format, ensure it starts with http:// or https://'.
• Add per-tool examples in prompts or documentation showing typical multi-step workflows (search → read → extract) to guide LLM composition.

Inferred effective spec: <=2025-11-25.

• Relies on Logging (deprecated) - log to stderr or use OpenTelemetry