Server has 7 tools with complete input schemas and descriptions. Naming follows verb_noun convention (generate_, review_, submit_, get_, cancel_). Descriptions are present but generic (avg 120 chars, below 194-char baseline). Parameters are typed and mostly described, but lack constraints (enums, ranges, patterns). Output schemas are not documented, LLMs cannot predict return structure. Error handling is absent; no recovery guidance. Security concern: model parameter is user-controllable, allowing arbitrary model selection without validation. Patch-focused domain is narrow but well-scoped.

• Document output schema for all tools. Example: generate_patch returns {summary: string, patch: string, tests: string[], assumptions: string[], risks: string[], usage: object}. Include field descriptions and types.
• Add enum constraint to model parameter: restrict to ['gpt-5.6-sol', 'gpt-5.5', 'gpt-5.4']. Validate server-side and return clear error if invalid.
• Add error handling documentation: describe success response structure, error codes (e.g. 'invalid_model', 'context_too_large', 'job_not_found'), and recovery actions for each.
• Enhance parameter descriptions with format/constraint guidance. E.g. 'allowed_paths: List of relative repository paths (no absolute paths, no '..' traversal). Example: ["src/main.py", "tests/"]'.
• Add result limits and pagination to job result tools. E.g. 'get_patch_result returns patch up to 500k chars; if truncated, include a next_offset token for continuation.'