Server has 4 tools with basic schemas and descriptions, but significant gaps prevent production use. All tools have input schemas with type definitions and descriptions, meeting minimum requirements. However, descriptions are generic (10-50 chars), parameter descriptions lack detail about constraints/formats, and output schemas are completely undocumented. No error handling guidance, no pagination support despite list operations, and no tool annotations. Tools return formatted strings rather than structured objects, forcing LLMs to parse unstructured text. Context storage pattern is non-standard and not exposed to the agent.

• Convert all tool outputs from formatted strings to structured JSON objects. Return {repo_name, owner, stars, forks, language, updated_at, url} instead of a formatted string. This enables LLMs to extract fields for chaining and reduces token waste.
• Add enum constraints to parameters: state in github_list_issues should be {type: 'string', enum: ['open', 'closed', 'all'], default: 'open'}. Add min/max to max_results: {type: 'integer', minimum: 1, maximum: 100, default: 5}.
• Implement proper error handling with actionable messages. Instead of 'return None', return structured errors: {error: 'repo_not_found', message: 'Repository owner/repo not found. Verify owner and repo names are correct.', retry: true}.
• Add pagination to github_list_issues. Accept limit (1-100, default 20) and page (default 1) parameters. Return {issues: [...], total_count, page, has_more} so agents can iterate through large result sets.
• Expand tool descriptions to 50-150 chars explaining WHEN to use each tool. E.g., 'Fetch metadata for a GitHub repository (stars, language, last update). Use this to verify repo exists before listing issues or to display repo info to the user.'